Thanks! I’ll explore potential bottlenecks and failure modes of your current AI summary tooltip design when handling large AG Grid datasets (10,000 to 1 million+ rows), and then provide a range of strategies to make it scale effectively without sacrificing UX or LLM performance. I’ll also consider architectural and LLM prompt/streaming optimizations tailored for your Vercel + OpenAI setup.

I’ll report back shortly with practical options.

Scaling LLM-Based Summaries for Large Datasets

Introduction: Implementing an AI summary tooltip in a data grid provides users with quick insights, but scaling this to massive datasets (tens of thousands to millions of rows) introduces significant challenges. The current approach – sending all active column values to an OpenAI model via the Vercel AI SDK – works for small tables, but breaks down as data grows. We need to analyze where this design fails at scale, and explore both frontend and backend strategies to make the summary feature robust for any dataset size. This report first identifies the failure points (latency, token limits, etc.), then presents a range of techniques (sampling, aggregation, chunking, clustering, local models, etc.) for scalable summarization. We also discuss prompt engineering tactics for long tables, architectural recommendations (aligned with Vercel AI SDK and OpenAI’s APIs), and UX best practices (such as guiding users to filter or group data when needed). All suggestions are backed by best practices and recent developments in LLM applications.

Challenges and Failure Points at Scale

Even before implementing new techniques, it’s critical to understand why the current “send all data to LLM” approach fails for large datasets. Key failure points include:

Context Window Limits: OpenAI models have fixed context sizes (e.g. ~4K tokens for GPT-3.5, up to 32K for GPT-4). A dataset with 10,000 rows easily exceeds these limits, meaning the model cannot even see all the data at once. Simply moving to a larger context model only delays the problem – for example, ~10K tokens of input would already cost around $0.60 with GPT-4-32K, and truly large datasets (100K+ tokens) are beyond current limits. In short, you cannot feed millions of data points directly to the model due to hard token constraints and cost scaling.
Latency and First-Token Delay: As dataset size grows, the time to prepare and send the prompt and for the model to begin responding increases greatly. Loading hundreds of thousands of values into a prompt is slow, and the model’s processing time grows non-linearly with input length. This leads to unacceptable latency (potentially tens of seconds or more) before the user sees any response. Maintaining <5 seconds to first token becomes impossible without changes. Streaming the response can mask some latency, but if the model is bogged down summarizing a massive prompt, even chunked streaming won’t start quickly.
Browser Memory and Performance: On the frontend, attempting to assemble a prompt string with millions of cells could crash the browser or exhaust memory. AG Grid may efficiently virtualize rendering, but if the code tries to iterate through every row to build a prompt, the UI thread could freeze. There’s also network overhead in sending such a large payload to the backend.
OpenAI API Limits and Costs: Very large prompts run into API payload size limits (beyond just the model’s context window). Even if the context limit weren’t an issue, sending megabytes of data per request is not practical. The cost of summarizing huge inputs frequently would be prohibitive, especially if multiple users invoke the tooltip. Rate limits could also be hit quickly if the system tries to chunk and send many requests in parallel.
Accuracy of Numerical Aggregation: LLMs are not reliable at performing precise calculations or aggregations over large lists of numbers. If asked to compute averages or counts from raw data in the prompt, the model may guess or hallucinate patterns. For example, attempts to have a model calculate an average from many data points often yield incorrect results. So even if we cram all data into the prompt, the summary might be wrong on quantitative details. Relying on the LLM alone for numeric computations is risky.
Hallucination and Omission: With overwhelming input, an LLM might ignore or misinterpret parts of the data. It could miss small but important subsets (e.g. an outlier or minority category) or hallucinate trends that aren’t actually present if the prompt is truncated or skewed. The more data provided, the harder it is to ensure the model focuses on the truly important bits. There’s a design risk that the summary becomes too generic (“data varies across many entries”) or includes false statements, if the prompt isn’t carefully constrained.
User Experience Issues: From a UX perspective, a tooltip that takes too long is unusable. If the summary feature fails or stalls on large data, users may be left without feedback. Moreover, showing a summary that is incomplete or “too high-level” because of data volume might confuse users if we don’t set expectations. There’s also the question of when to trigger the summary; doing it automatically on a huge table could be disruptive, so user-initiated triggers with clear loading indicators are needed.

Understanding these risks guides the solutions: we need to reduce and preprocess the data before sending to the LLM, to stay within token limits and latency bounds, and use the LLM for what it’s good at (language and pattern description) while offloading what it’s bad at (heavy computation). With these failure modes in mind, we can now explore techniques to make the AI tooltip scale.

Techniques for Summarizing Large Data at Any Scale

There is no single silver bullet to handle millions of rows, but a combination of strategies can ensure the summary tooltip remains useful and fast. Broadly, the approaches fall into data reduction, multi-step summarization, and optimized modeling. Below, we break down the techniques into categories, discussing how each helps and how they can be implemented (often in combination):

1. Data Sampling and Row Selection

Core idea: Instead of sending the entire dataset, send a representative subset of rows to the LLM. This drastically reduces token usage and latency. The simplest form is random sampling – pick, say, 500 or 1000 rows at random from the table and ask the model to summarize those as a proxy for the whole. This keeps prompt size manageable regardless of original data size.

Random Sampling: Randomly sampling rows preserves a bit of every part of the data, which can retain diversity. The model might see common patterns present in the data without being drowned in repetition. However, pure random sampling risks missing important rare events or outliers. For example, if 5% of the dataset are error cases or a small department, a random 1% sample might or might not include them. Also, as Deepchecks notes, sampling (like truncation) “may result in loss of contextual information, and reduce the quality of the generated output”. To mitigate this, consider stratified sampling.
Stratified or Guided Sampling: Ensure that the sample includes proportional representation from key categories or ranges. For example, if the data has a column “Department” with 10 departments, take some rows from each department. Or bucket a numeric column into high/medium/low ranges and sample from each. This way, the summary won’t completely ignore small groups. The sampling strategy can use domain knowledge: e.g. always include the top 10 and bottom 10 values for critical metrics (“top-k selection”). Top-k selection means explicitly including a small number of most extreme or important records – e.g. the highest-paid employee, the oldest entry, the largest transaction – which ensures the model sees noteworthy points. These can be combined with random picks of typical entries for balance.
Maximum Coverage Sampling: Another approach is to choose examples that maximize coverage of variability in the data. This can be done via clustering (discussed later) or simpler heuristics. For instance, you could sort the data by one key metric and take evenly spaced samples (to cover low, median, and high values). Or detect unique subgroups (e.g. if pivoted by region, take some rows from each region).
Limit Columns or Fields: Row sampling can be paired with feature selection. If certain columns are irrelevant to high-level trends (or contain verbose text that would bloat the prompt), consider omitting them from the prompt for large-data scenarios. For example, an “ID” field or a free-text comment might be skipped to save tokens, focusing the LLM on the core dimensions and metrics. The tooltip summary likely doesn’t need every column to produce useful insights.

Implementation: Sampling can be done on the frontend (since the grid has the data). For large datasets, doing this in a web worker is wise to avoid blocking the UI. Alternatively, the backend API route can accept a query for a random subset (if the data is too large to send entirely to the client anyway). The key is to implement a threshold (e.g. if row count > N, sample instead of using all). Be transparent in prompt that a sample is being used: e.g. “(The following is a sample of 500 rows from the dataset)” so the model doesn’t over-generalize or assume it’s the full data. Despite its simplicity, sampling is often the first line of defense to keep the problem tractable, and works especially well when the data is fairly homogeneous or patterns are strong (a random sample will still capture them). Just keep in mind that important outliers might be missed – which is why combining sampling with other techniques (like pre-aggregated stats or clustering) can yield a better summary.

2. Pre-Aggregation and Statistical Summaries

Core idea: Instead of (or in addition to) sending raw records, pre-compute summary statistics or aggregates and feed those to the LLM. This leverages conventional data processing to handle scale and lets the LLM focus on explaining the summary, not calculating it. Essentially, we turn the big dataset into a smaller report of key figures, which the LLM then narrates.

Global Statistics: Compute overall metrics such as count of rows, numeric column averages, medians, min/max values, etc. For categorical columns, compute frequency counts of each category (or at least the top N categories by count). For dates, find the date range. Many of these can be obtained quickly via a database query or in-memory computation. For example, if the table has a “Salary” column, calculate the minimum, maximum, mean, perhaps quartiles, and standard deviation. These numbers can be inserted into the prompt: “The dataset has 100,000 rows. The average Salary is $65K (min $30K, max $200K). There are 5 departments, with counts: Sales (40K), Engineering (30K), ... etc.” An LLM can take this concise profile and produce an insightful summary (it might say “Sales is the largest department, and the highest salary is $200K, indicating ...” etc.). Crucially, the LLM isn’t left to guess these facts – they are provided, so the summary will be accurate.
Group-wise Aggregation (Pivoting): Often, summarizing comparative statistics is useful. If the data can be meaningfully grouped (e.g. by a categorical field like Region, Department, Product Category), do a grouped aggregation. This is effectively what a pivot table does: e.g. compute each department’s total headcount, average performance score, or sum of revenue. Providing the LLM with a small pivoted summary table (or a textual description of each group’s stats) is far more scalable than raw data. In fact, if the UI already allows pivoting in AG Grid, you can leverage that: when the user pivots the table, the dataset size is reduced to aggregated rows, which the LLM can summarize easily (“Group A has X value, which is higher than Group B’s Y value…”). Even if the user hasn’t pivoted, the system could automatically aggregate by a sensible dimension when data is too large, as a way to condense information. (For example, if there is a “Date” column, perhaps aggregate by year or month to summarize trends over time, rather than listing every day’s data.)
Descriptive Analytics in Code: We can employ statistics or simple algorithms to extract interesting tidbits: e.g. find any outlier values (using standard deviation or percentile thresholds), detect if any correlation is high (though in a table summary context, correlation might be too advanced unless we specifically code it). We could identify “the top 5 highest values of metric X are ...” or “90% of entries fall into 3 categories”, etc. These findings can be fed to the LLM or even directly phrased to the user. Essentially, do as much deterministic analysis as possible with code, and let the LLM focus on turning that into natural language and possibly drawing implications.
Accuracy and Efficiency: By pre-aggregating, we ensure numbers are exact (no risk of the model mis-calculating). Databases and in-memory engines are optimized for these operations even on millions of rows. For instance, a SQL query or a library like DuckDB or pandas can compute summary stats in seconds. This can be done server-side (if the data originates on a server/DB) or client-side if the data is already in the browser (WebAssembly versions of analytics engines, or using AG Grid’s built-in aggregation functions). Sigma Computing’s best practices for LLM in BI similarly suggest cleaning and trimming the data within the limit and applying filters/aggregations to ensure inputs comply with token limits.
Prompt Integration: Once we have these stats, construct a prompt that gives the model context about the dataset and the computed highlights. For example: “Summary of dataset: 1) Size: 250,000 rows spanning 2015-2025; 2) Departments: Sales (40% of entries), Engineering (35%), HR (5%)...; 3) Salary: avg $65K (min $30K, max $200K); 4) Tenure: most employees have 3-5 years tenure; 5) Notable: 2 employees have exceptionally high salaries and belong to Engineering.” Then ask: “Provide a concise summary of the key insights from this data.” The model can use these bullet points to produce a human-friendly summary. This approach sends perhaps a few hundred tokens of prepared info, instead of thousands of raw data points.

In essence, pre-aggregation transforms the problem from “summarize this huge raw data” to “summarize this already distilled report”. It aligns with how an analyst might summarize: first compute high-level metrics, then describe them. Best of all, it guarantees fast performance (since computing aggregates is O(n) and can be done in optimized native code) and keeps the prompt size small and stable.

3. Hierarchical Chunking and Map-Reduce Summarization

Core idea: When the dataset is too large to summarize in one go, break it into chunks, summarize each chunk separately with the LLM, then summarize those summaries. This hierarchical summarization (also known as a Map-Reduce approach in the context of LLMs) allows arbitrarily large data to be handled through multiple passes. It’s a common strategy for summarizing long documents and can be adapted to tabular data.

Chunking the Data: Split the full dataset into smaller subsets that fit the model’s context window (for example, 5,000 rows per chunk). The chunks could be random splits, or time-based (e.g. split a log by year), or category-based (e.g. chunk by region). It’s often beneficial to preserve some logical grouping in chunks to avoid losing context – for instance, rather than pure random chunks, maybe each chunk is “all data for one department” if that yields balanced sizes. Each chunk on its own is manageable for the LLM.
Summarize Each Chunk: For each subset of rows, generate a partial summary. This can be done in parallel if you have the resources (e.g. fire off multiple OpenAI requests concurrently, one per chunk) to save time, or sequentially if needed. The prompt for each chunk would be similar to the current implementation but just limited to that subset. You might include a chunk identifier in the prompt if needed (e.g. “Summary of Sales Department data (10,000 rows): …”) so that later you know which summary corresponds to which part (this is useful if the combination step might want to preserve some structure).
Combine Summaries (Reduce step): Once you have (for example) 10 summaries of 10 chunks, you then ask the LLM to summarize those summaries into a final overview. This second-level prompt is much smaller (10 paragraphs maybe, one per chunk summary). The LLM will produce a higher-level summary that hopefully covers the entire dataset’s themes. This two-tier process is essentially how map-reduce text summarization works: “map” each chunk to a summary, then “reduce” the summaries into one. LangChain’s map_reduce chain is an example implementation that “divid[es] the document into chunks, generating summaries for each, and then combining these summaries to create a final summary”.
Recursive or Multi-level Summaries: If the data is extremely large, you could even have more than two levels (e.g. summarize 100 chunks into 10 intermediate summaries, then one final summary). But usually one intermediate layer is sufficient for most practical sizes. The key is each summary is much shorter than the raw chunk, so information is being condensed at each step.
Quality and Consistency: A challenge with this approach is ensuring the final summary doesn’t miss important details. If something significant appears in only one chunk’s data, it must appear in that chunk’s summary to have a chance of making it to the final. You can guide chunk-level prompts to surface certain info (e.g. “In the summary, note any unusual values or major trends in this subset”). Overlap techniques can help – sometimes overlapping chunks slightly or repeating critical rows in adjacent chunks can ensure continuity, though in data tables it might not be as relevant as in text. Also, combining summaries can lead to some repetition or disjointed coverage. The refine approach (where you sequentially feed chunk summaries and refine the overall summary) is another option, though it is more sequential and slower. The map-reduce (parallel) approach is usually faster since chunk processing can be parallelized, at the cost of some loss of global coherence.
Streaming Considerations: With chunking, you won’t stream the final answer until all chunk summaries are done (since you need them to produce the final). This can increase latency. One way to mitigate the wait is to stream intermediate results to the user (perhaps not as the final answer, but as a “progressive summary”). However, that might confuse the user, so typically you’d show a loading state until the final summary is ready, then stream that. To keep within a <5s first token budget, you might need to limit the number of chunks or use smaller models for chunk summaries. If you have, say, 4 chunks processed in parallel, and each returns in ~3 seconds, then a quick reduce call might yield the first token by ~3-5 seconds. On the other hand, 100 chunks sequentially would be far too slow. Therefore, chunking works best when the data can be divided into a moderate number of large chunks (e.g. 5–20 chunks), rather than hundreds of tiny pieces.
Tools: This approach can be orchestrated using libraries like LangChain (which provides out-of-the-box chains for summarization using map-reduce logic) or LlamaIndex (which can build a summary index by breaking documents into nodes and summarizing hierarchically). These can save some boilerplate in implementing the recursive summarization. However, implementing it manually with OpenAI API calls is straightforward too: just manage the chunking and then call the model twice (once per chunk, once to combine results). It’s important to monitor each summary length so that the final combination prompt doesn’t itself become too long (you might need to ask for concise chunk summaries).

In summary, hierarchical chunking guarantees scalability: you can always break the data into pieces small enough for the model. It sacrifices some immediacy and maybe some fidelity, but is a robust fallback when faced with extremely large inputs. As one Medium tutorial put it, this method “is designed for summarizing large documents that exceed the token limit… dividing the document into chunks, generating summaries for each chunk, and then combining these summaries”. The same concept applies to a table considered as a “long document” of rows.

4. Semantic Clustering and Representative Selection

Core idea: Use embedding-based clustering to identify groups of similar data points, then summarize only those representative points instead of the entire dataset. This leverages AI not to directly summarize, but to organize the data by similarity first, drastically reducing redundancy in what the LLM has to read. Essentially, if many rows are “saying” the same thing, we only need the model to see one or a few of them.

Embedding the Data: Represent each row (or each group of rows) as a vector embedding that captures its content or characteristics. For numerical data, one might create a feature vector (normalized values of key metrics). For textual or categorical data, you can use a pre-trained text embedding model (OpenAI’s text-embedding-ada-002 or similar) to get a vector for each row’s description. The idea is that similar rows will have embeddings close together in vector space.
Clustering Algorithm: Apply a clustering algorithm (like K-means or hierarchical clustering) to these embeddings. This will partition the rows into K clusters based on similarity. Rows in the same cluster are presumably about the same “topic” or pattern. For example, in an organizational dataset, one cluster might group engineers (similar salary range and department), another cluster might be salespeople, another might be interns, etc., discovered automatically. Common algorithms like K-means are effective and there are libraries (e.g. scikit-learn in Python, or even in JS via WebAssembly or calling a backend) to do this efficiently even for large sets (though embedding 1M points might be heavy – one might sample or use dimensionality reduction first).
Representative Selection: For each cluster, select one or a few representative rows. Often the centroid (the synthetic average point) isn’t an actual row, so instead we pick the actual row closest to the centroid (the most “typical” member of that cluster). This representative is presumed to stand in for all the similar rows in that cluster. We may also take the most extreme member of a cluster if needed to show variance. The Medium article on centroid-based vector selection (CBVS) describes this approach: embed each document, cluster them, pick the item closest to each cluster center, summarize those, then summarize the summaries. The Eagerworks blog similarly outlines: chunk and embed a large text, cluster the embeddings, choose the most representative chunk from each cluster, and then feed those to an LLM for summarization. By doing so, “each cluster represents a distinct aspect or topic... by focusing on the central idea of each cluster (centroid), we capture the essence of multiple paragraphs in one condensed form”. In our context, each cluster of rows, and its representative, captures a theme in the data.
Summarize Representatives: Now instead of summarizing hundreds of thousands of rows, we prompt the LLM with just the representative examples (maybe one per cluster, or the top few per cluster if clusters are broad). For instance, if we chose 10 clusters, we have ~10 rows to show. We can list those representatives (or a brief description of each cluster’s characteristics) and ask the LLM to summarize the overall patterns. The LLM will effectively be summarizing the cluster “centroids”, which should yield a comprehensive summary touching each major group. If needed, we could first ask the LLM to summarize each cluster separately (especially if clusters themselves have a lot of info), then combine – but often just giving the representative points from all clusters in one prompt is enough since that total is small.
Benefits: This method is powerful because it removes redundancy. If your dataset has a lot of similar entries (which is common in large data), clustering means you don’t waste tokens describing each similar item repeatedly – one prototype speaks for many. It’s also adaptive: if the data has natural groupings, the summary will naturally mention those groups. Clustering can also surface outliers as their own cluster. For example, an unusual data point may end up alone in its cluster, thus it will definitely be included as a representative (whereas random sampling might miss it). This approach was shown to reduce token usage and cost by summarizing “only the most representative paragraphs” in a document – translating that to cost savings for us by limiting how much the LLM needs to see.
Trade-offs: The clustering process itself has a cost (computationally and possibly monetarily if using an API for embeddings). For very large data, one might do this offline or one-time. If the dataset is relatively static, you could preprocess embeddings and clusters once and reuse them for summaries. If the data changes frequently or is user-dependent, that’s trickier. For mostly numeric data, simpler clustering (like K-means on key columns) can be done quickly without expensive embeddings. If using OpenAI embeddings for a million rows, note that’s a million API calls (or a batched call for many, still significant cost) – probably not feasible on the fly. So this technique might be reserved for when you can justify a heavy precomputation or if the dataset size is moderate (tens of thousands of rows, which is easier).
Tools: scikit-learn (Python) or TensorFlow.js / faiss (for vector similarity) can perform clustering. There are also vector database services (Pinecone, etc.) that can store embeddings and do clustering or nearest neighbor search, which could help find representatives without clustering explicitly (e.g. pick points that maximize diversity via vector search). The approach outlined by Vishal Khare in Mar 2024 uses LangChain for embedding and KMeans for clustering, achieving a summary of 206 reviews by clustering into 8 clusters and summarizing those representatives. Another reference describes chunking and then “within each cluster, choosing the most characteristic paragraph (usually the centroid) to encapsulate the idea” and summarizing them. These references reinforce the viability of clustering for summarization.

In practice, clustering could be combined with the hierarchical approach: e.g. cluster the data into 10 clusters, then for each cluster, if it’s still large, either sample or chunk-summarize within that cluster to get a mini-summary, then combine those. This hybrid would ensure even very heterogeneous data is covered. But even using clustering alone, you often get a good spread of the data’s “story” to feed the LLM.

5. Local LLMs and Extended Context Models

Core idea: When external API limits are a bottleneck (due to token limit, cost, or privacy), consider using alternative model setups – either local/self-hosted LLMs with custom capabilities, or models that offer larger context windows. This strategy doesn’t reduce the data per se, but can extend the system’s ability to handle more data or avoid sending raw data out.

Extended-Context Models: Newer LLMs are emerging with very large context windows (100K tokens or more). For example, Anthropic’s Claude model can accept around 100K tokens of input. If you have access to such a model, it might handle a significantly larger chunk of the dataset in one go than GPT-4’s 32K. In 2025, we might see even more models that can consume entire books or datasets. Using these could allow summarizing, say, a 50,000-row dataset without elaborate chunking. However, using a giant context has downsides: performance may be slower and cost scales linearly with tokens (Claude 100K or GPT-4 32K are expensive per call). As a result, even if you can technically fit the data, summarizing 1M rows by brute force would cost a fortune and still be slow. So this is a partial solution – great for moderately large inputs (maybe up to tens of thousands of rows) if you want simplicity, but not sufficient for the worst-case scales. One could adopt a policy: if data is slightly above normal limit, use GPT-4-32K; if it’s way above, then apply other reduction techniques anyway.
Local or Self-Hosted LLMs: Deploying a local model (like LLaMA 2, GPT-J, etc.) within your infrastructure can alleviate concerns about sending sensitive data to external APIs and can be customized. You could fine-tune a local model on summarizing structured data or allow it to ingest larger inputs by modifying its architecture (some research models can have 64K+ token position embeddings). Running a local model also means no token-based cost per query, though you pay in compute resources. For a SaaS product, a powerful local model could serve many requests with predictable cost. That said, the quality of open models might lag behind OpenAI’s for nuanced summaries, and engineering a local solution (serving via GPU, etc.) is non-trivial. Still, for an internal summarization service, a fine-tuned model could be very effective at, say, generating bullet-point summaries of CSV data.
Local Hybrid Processing: You can combine local processing with LLM reasoning through techniques like OpenAI Function Calling. For example, you could have an OpenAI model orchestrate the summary by calling local functions: the model could decide “I need the distribution of values for Column X” and call a function that returns that info (computed from the full data), then incorporate it into the summary. This way the heavy lifting is done by code, and the model only asks for specific pieces of data. This is akin to a retrieval approach where the model retrieves stats instead of raw text. It requires more complex prompt engineering (defining functions the model can call, etc.), but it’s a scalable architecture: the model never sees more than the data it explicitly requests. In an interactive setting, this could even be done with an agent: e.g. using a library like LangChain or GPT-Engineer that allows the LLM to iteratively query a dataset. However, this might be overkill for a tooltip feature – a simpler implementation might be a two-step: first use code to compute needed facts, then feed them to a model (which is essentially what we described in pre-aggregation).
Privacy Considerations: If the data is sensitive (organizational design info might be confidential), using local models or Azure OpenAI (which offers a controlled environment) could be required by some clients. Summaries often contain derived information which is less sensitive than raw data, but even so, the safest route is not to send raw data externally. A compromise could be to use OpenAI for small data (fast to implement) but automatically switch to a local summarizer for very large or sensitive datasets.
Tooling: There are frameworks like Haystack or LlamaIndex that can facilitate running queries/summaries on private data with local models or via retrieval. Also, open models like MPT-100k+ (by MosaicML) can handle long inputs. You might mention to stakeholders that models like Longformer or LED (Longformer Encoder-Decoder) can handle 16K+ tokens natively, and research is ongoing into models that directly take very long sequences. As of now, a pragmatic approach is often a combination: use local code for data crunching, and a strong but limited LLM for the summary text.

In summary, exploiting model choices (bigger context or self-hosted) can extend the capability, but they should be paired with the smart data pruning techniques above for best results. Using a local model might allow you to implement a custom pipeline (like the clustering + summary) entirely in-house without API calls. Always weigh the engineering effort and quality trade-off – often a simpler approach (like sampling + GPT-4) might outperform a complex local solution unless data size or privacy absolutely demands it.

6. Retrieval-Augmented and Query-Based Summarization

Core idea: Treat summarization as a knowledge retrieval problem – instead of dumping data to the model, allow the model to retrieve small relevant pieces of data in order to answer the “summarize this” query. This approach turns the one-shot summary into a series of targeted queries the AI can make against the dataset, akin to how an analyst might ask specific questions to form a summary. It’s more complex to implement but very powerful for maintaining detail with unlimited data.

Embedding + Vector Search (RAG): One technique is to index all rows (or row clusters) in a vector database using embeddings. Then, when a summary is requested, formulate several guiding questions or prompts and retrieve the top relevant rows for each, then have the LLM summarize those. For example, you might prompt internally: “What are the main categories or groups in this data?” – use that as a query embedding to pull a few exemplars for each major group from the vector index; “What are notable extreme values or outliers?” – retrieve rows that are outliers; “What trends over time are present?” – retrieve some representative time slices. Each retrieval yields a small context which the LLM can summarize or answer, and then those answers can be collated into a final summary. This essentially uses the LLM in a question-answering loop over the data. In implementation, you might need to predetermine the questions or use an agent that dynamically decides which queries to run (which is more advanced).
Text-to-SQL or Query Execution: Another variant is allowing the LLM to issue SQL queries or use a data API to get aggregated results. This is akin to an agentic approach where the LLM has a tool (the database) to pull facts. For example, the LLM might be instructed: “You have access to a database of this table. To produce a summary, you can ask for counts, sums, etc., by providing SQL queries. Then you will use the query results to compose the summary.” The LLM might generate a query like SELECT Department, COUNT(*) FROM data GROUP BY Department; (if it wants to know sizes of groups), get the result (counts per department), then incorporate that into its answer. This method was suggested as the right approach for queries on large data in an OpenAI forum discussion – “LLMs are good at generating text but not good with numbers… Text-to-SQL is the way to go” for data questions. In our summary scenario, we could predefine a set of SQL queries to run (like we did in pre-aggregation manually) or indeed have the model do it interactively. Microsoft’s Autogen or other agent frameworks can sometimes automate the plan (the model first produces a plan of what it needs, then queries, then summarizes).
Function Calling: OpenAI’s function calling feature allows the model to explicitly request information via a function. We could define functions like get_column_stats(column_name) or get_top_rows_by(column_name, n) and so on. The model, when faced with a huge table summary, could call these functions (the backend executes them on the full dataset) and return a JSON of results, which the model then uses to compose a narrative. This approach divides the task: the model decides what to ask for and how to explain it, while the heavy data computation happens in the function implementation. It ensures we never feed the model more data than it specifically asks, keeping token usage minimal regardless of dataset size. Implementing this requires careful prompt design (so the model knows these functions exist and when to use them) and robust function implementations. But if done well, it can yield very accurate and detailed summaries, because the model can dig into the data step by step.
When to use: This retrieval-based approach is likely overkill if a quick aggregate or sampling will do, but it shines when users might ask more detailed follow-ups. For example, after an initial summary, a user might ask, “Which department has the highest turnover and why?” – an agent could then specifically query that. In the context of the tooltip, if it’s purely one-shot, a simpler pipeline might suffice. But thinking ahead, this kind of architecture could enable interactive AI exploration of the dataset beyond just a static summary. It aligns with the concept of conversational data analysis (as Quadratic’s blog noted, allowing follow-up questions for deeper insight).

In summary, retrieval-augmented summarization turns the problem inside out: instead of compressing data upfront, it lets the LLM pull the data it needs. This ensures the model never drowns in irrelevant info and can theoretically scale to any dataset size (since each retrieval is small). The downside is complexity and ensuring the model knows how to formulate the right queries. It might be something to consider for future enhancement, but even if not implemented immediately, keeping this paradigm in mind will help integrate the AI summarizer with your data backend in a maintainable way.

Prompt Design Strategies for Long-Table Summaries

Crafting the prompt (or prompts) effectively is just as important as algorithmically cutting down the data. When dealing with a large dataset (even after reduction techniques), the prompt should guide the LLM to produce a useful and coherent summary. Here are prompt engineering best practices specific to summarizing tables:

Provide High-Level Context: Always inform the model about the overall context of the data. For example: “You are analyzing a table with 500,000 rows of employee data from 2015–2025.” Mention what a single row represents (if obvious: e.g. “Each row is an employee’s record including their department, role, salary, and tenure.”). This helps the model understand the task scope. As the Juice Analytics example showed, a first step is to clarify what each row means. In a prompt, you might not list every column, but do summarize schema or meaning: “Columns include Department, Role, Salary, etc.” This primes the model’s expectations and reduces confusion.
Explicitly Instruct on Summary Focus: By default, a model might try to enumerate lots of things or get lost. Tell it exactly what kind of summary is needed. For instance: “Summarize the key insights and trends in the dataset. Focus on overall patterns, differences between groups, and any outliers. Do not list every data point, but provide an aggregate overview.” This steers the model away from regurgitating many values and towards a descriptive analysis. If the table is pivoted or aggregated already, mention that: “The table is pivoted by Department, so each row represents a department’s totals.” This way it knows to compare departments in the summary. Essentially, treat the model like a junior analyst: give it instructions on how to approach the summary (much like you would guide a person: “look at distributions, compare X and Y, note extremes, etc.”).
Use Bullets or Structure for Clarity: Often, instructing the model to output in a structured format (like bullet points or numbered list of insights) yields a clearer result for the user. For example: “Present the summary as 3-5 bullet points, each highlighting an important insight (such as a comparison, an extreme value, a trend, etc.).” This prevents the model from rambling and ensures the content is scannable. It also naturally limits length. The function calling or chain approach from Juice Analytics actually had the model output in steps, which they then consolidated. While you might not show the steps to the end user, you can still apply that logic internally or via prompt: e.g. instruct the model to first identify categories, then give stats, then highlight nulls, etc., and finally output a concise summary. However, in a single-pass scenario, a well-structured prompt and asking for bullet points may suffice.
Include Key Stats in Prompt: If you have computed some stats or selected representatives (from the techniques above), embed them in the prompt clearly. You can list them as bullet points or a short table. Models handle tabular or bulleted numeric info quite well and can reason over it. For instance: “Dataset highlights: - Total rows: 50,000; - Time span: Jan 2010–Dec 2020; - Departments: Sales (20k rows), Tech (15k), HR (5k), ...; - Avg Performance Score: 3.8/5; - Highest Salary: $250k (CTO), Lowest Salary: $30k.” After presenting these, you might add: “Using the above information, summarize the company’s workforce composition and any notable findings.” By giving the model these anchor facts, you reduce chances of error and give it something concrete to weave into the narrative.
Token Limit Warnings: If you are forced to truncate or omit some data in the prompt, you can mention this to the model so it doesn’t erroneously assume completeness. For example: “(Data was truncated due to length; focus on general trends rather than exact figures.)” This can make the model more cautious in its statements. In general, being transparent to the AI about any limitations can help it avoid going into details that might not be supported by partial input.
Few-Shot Example (if feasible): If you have the budget and context room, providing a small example of a table and its summary can train the model to respond well. For instance: “Example:\n[Small table data]\nSummary of example: ...” then “Now summarize the following dataset:”. This is optional and context-heavy, so it might only work for moderate data sizes or if you use GPT-4-32K. But it can significantly improve quality if the model sees what you consider a good summary. Alternatively, you might just describe what a good summary should include (as done above with instructions).
Avoid Ambiguity: Make sure the prompt doesn’t leave the model guessing what the user wants. Words like “summarize” can be interpreted in many ways – we clarify by saying “key insights”, “overall trends”, etc. If there are specific things not to do, state them (e.g. “Do not output a list of every department’s value, only mention the top few for context.”). The Quadratic blog emphasizes that you must not assume the AI will infer what you don’t explicitly tell it. So, if the user expects certain insights (like identification of the largest group, or noting an anomaly), it’s worth hinting that in the prompt.

Using these prompt strategies ensures that whatever data we do send to the LLM is utilized effectively to produce a meaningful summary. A well-structured prompt can dramatically improve the output quality for data analysis tasks. In tests, prompt specificity can turn a vague output into a pinpointed analysis. Always remember that the AI “thinks” in words – the better we communicate our requirements in the prompt, the better it will articulate the summary.

Architectural Recommendations (Vercel AI SDK and OpenAI Integration)

Designing the system architecture to incorporate the above techniques requires decisions about what happens on the frontend vs backend, how to maintain speed, and how to leverage the Vercel AI SDK for streaming. Here are recommendations for an architecture that keeps the solution scalable and user-friendly:

Client-Side vs Server-Side Processing: Leverage the strength of each environment. The browser (client) is great for things like filtering and quick aggregate computations on data already loaded (since AG Grid likely holds the data, or can retrieve it). The server (Vercel Edge Function or API Route) is better for heavy computations that the client can’t handle or for accessing secure data or databases. A hybrid approach works well:
- On the client, you can implement threshold checks and lightweight sampling. For example, if the dataset is under, say, 5,000 rows, maybe just send it all (or a large sample) directly to the AI API. If it’s larger, the client could automatically sample or aggregate and only send the reduced payload. The client can also compute simple stats (count, min, max, etc.) using JavaScript or a library (AG Grid’s API, or a small DataFrame utility). Modern JS with Web Workers can handle tens of thousands of records easily for such computations. If the data is extremely large (millions of rows) and not all loaded in the browser (perhaps fetched on the fly), then the backend might need to do those computations via a database query or similar.
- On the server (Vercel function), do the final assembly of the prompt and the call to OpenAI. The client can send either the raw subset of data or precomputed stats to the server. For instance, the client might send: { rowSample: [...1000 sampled rows...], stats: {...} } to the API. The server then crafts the prompt (inserting those numbers or sample values appropriately) and invokes the OpenAI API via the Vercel AI SDK. Doing the OpenAI call server-side is good for security (hiding the API key) and allows using the streaming support of Vercel’s SDK. Keep in mind that Vercel serverless functions have execution time limits (usually on the order of 10s of seconds). The approach should ensure we don’t exceed that – which means avoid extremely long sequential processes in one request.
Streaming Response: Use the Vercel AI SDK’s streaming to send tokens to the frontend as they come. This greatly improves perceived performance – even if the whole summary takes 8 seconds to generate, the user might see the first point at 2 seconds, which is encouraging. The SDK (via streamText or similar) makes it easy to implement this. The key is to start the OpenAI request as soon as you have the prompt ready. If you’re doing intermediate steps like multiple OpenAI calls (chunking) within the function, you won’t be able to stream until the final combination. In those cases, consider whether you can restructure as a single call or at least stream partial info. One idea: if chunk summaries are needed, you could stream each chunk’s summary to the client as it finishes (identifying it as interim output), and then finally stream the combined summary. However, that complicates the frontend logic. A simpler approach is likely: aim for one OpenAI call per user request whenever possible (by precomputing and condensing input beforehand). That way, the server just forwards the streaming response from that one call straight to the UI, achieving the <5s first token goal.
Parallelization and Performance: If you must use multiple OpenAI calls (like map-reduce summarization), try to parallelize them on the server. Node.js can handle concurrent promises, but keep an eye on rate limits and total compute. For example, fire off 4 chunk summary requests concurrently, then when all resolve, immediately feed their outputs into the final summary call. This could fit in a ~10 second window with streaming. The user would only see the stream once the final call starts, but overall wait is reduced compared to sequential. Vercel’s functions should support this pattern, but be mindful of memory (don’t hold huge arrays for all chunk data at once if possible). If parallel calls prove too heavy, an alternative is to pre-summarize in the background. For instance, when a dataset is first loaded or updated, you could trigger a background process that computes or caches a summary. Then the tooltip is near-instant as it just retrieves that. Vercel doesn’t have long-lived processes, but you could use something like Vercel’s Cron jobs or queue, or even piggyback on the data loading pipeline.
Caching and Memoization: If users often summarize the same dataset (or same filtered subset), implement caching. For example, cache the last N summary results keyed by data signature (dataset ID + filter conditions). The cache could be in-memory (within the function instance, though Vercel instances are ephemeral) or stored in an external store (Redis, etc.). Even caching at the client level is useful: if the user already got a summary and they haven’t changed the data or filters, reuse it. This avoids duplicate expensive calls.
Integrate with AG Grid Events: On the UX side, decide when the summary should refresh. If the user filters the grid or pivots it, you might want to regenerate the summary for the new view. Using AG Grid’s event hooks, you can detect such changes and trigger the summarization pipeline. Just ensure rate limiting – e.g. don’t re-summarize on every single scroll or minor change, only when a significant action completes (like user finishes adjusting a filter).
OpenAI Model Choices: Use the model appropriate to the data size dynamically. For small inputs, gpt-3.5-turbo (with 4K context) might suffice and is very fast/cheap. For moderate (e.g. up to 20K tokens of stuff after processing), maybe use gpt-3.5-turbo-16k. For larger or more complex language outputs, gpt-4 (8k or 32k) might produce better quality. You can implement a logic: if summary prompt token count < X, use model Y, else use model Z. Keep an eye on OpenAI’s token counting (you can estimate using tiktoken library to ensure you stay under limits). The user likely doesn’t need to know which model is used when, but using simpler models for easy cases can save cost and latency.
Error Handling and Fallbacks: Plan for failure modes. If the OpenAI API call fails or times out (perhaps due to too large input still, or network issues), the tooltip should handle this gracefully – maybe showing “AI summary not available for this dataset” or an apology message, rather than hanging. Also consider a fallback prompt: if the fancy prompt with stats somehow returns an error, perhaps try a simpler prompt or smaller sample automatically. Logging these incidents (maybe to console or a monitoring service) can help refine the approach over time.
Security: The backend should scrub any sensitive PII if that’s a concern before sending to OpenAI. Or use their tools to redact content. Since organizational data can be sensitive, ensure compliance with any data policy (perhaps allow customers to opt-out of sending data to OpenAI and use a local model path as discussed).
Open-Source Libraries: Incorporate libraries for efficiency where possible:
- LangChain – if using multiple steps or wanting to experiment with the agent approach, LangChain can simplify chaining calls and tools. However, adding it just for one prompt might be unnecessary overhead.
- LlamaIndex – could be useful if you choose to implement an index for retrieval-based Q&A on the data. It can manage chunking and retrieving relevant pieces for questions.
- DuckDB or SQL.js – to run SQL queries on the data either in the client or server. DuckDB-Wasm can execute SQL on a dataset in the browser (assuming the data can be loaded into it), which is neat for computing aggregates easily. On the server, if data is in a database, obviously use that (e.g. an SQL query for stats as part of the API logic).
- scikit-learn or ML libraries – for clustering or advanced sampling. You might not use these in production (especially not on the serverless function due to startup cost), but during development they can help prototype the clustering approach on a subset of data to see if it yields better summaries.
- tiktoken (OpenAI’s token counter) – to gauge prompt size. This could run in the Node backend to prevent accidentally overshooting model limits. For example, after assembling the prompt, count tokens; if it’s somehow too large, you could apply an emergency truncation or switch strategy (rather than get an API error).
Vercel AI SDK Streaming Usage: The Vercel guide suggests using their SDK’s toTextStreamResponse() which handles setting the correct headers and streaming format. Ensure that your Next.js API route (if using Next.js) is set to a dynamic route (Edge functions or Node depending on need) and not cached, since it’s streaming. Also, because Vercel Edge Functions run in a different runtime (often with limited packages), you might decide to use a Node.js serverless function instead if you require certain libraries (Edge is more limited but very fast for pure network I/O). The AI SDK should work in either, but heavy computation (like clustering 1M points) definitely suggests using a Node function (which can use native libs and possibly more memory).

In short, the architecture should adaptively choose a strategy based on dataset size and use a pipeline of: client pre-check -> optional preprocessing -> API call -> prompt assembly -> OpenAI streaming -> client UI update. By combining the frontend and backend strengths, you can keep the experience smooth. For instance, the client might quickly show, “Summarizing 1M rows by sampling 5% of data…” as status, then the server streams the result. This way users are informed and not left wondering.

Frontend User Experience Considerations

No matter how advanced the backend, the feature will fall flat if it doesn’t communicate well with the user. Large datasets especially require setting expectations with the user about what the AI is summarizing. Here are some UX guidelines to ensure the summary tooltip adds value without confusion:

Guide Users to Refine Data for Better Summaries: If a user has an extremely large or granular dataset, gently prompt them to filter or group the data for a more meaningful summary. This could be done via a tooltip message or UI hint. For example, if row count > 50k, instead of immediately running the AI, the tooltip could say: “Your dataset is quite large. Consider applying filters or grouping by a category for a more targeted summary.” The user could then choose to slice the data. You might even semi-automatically do it: e.g. present a dialog “Select a column to group by for summary” listing high-cardinality categorical columns. This way, the summary they get will be at a higher level (grouped) which is often more insightful than a summary of millions of disjoint points.
Automatic Thresholds: Define “safe thresholds” for automatically triggering the AI summary. For instance, if under 5k rows, run directly; if between 5k and 100k, maybe auto-sample and warn; if over 100k, require user confirmation or filtering. By not attempting obviously impractical summaries, you avoid frustrating the user. A hard cutoff (like disable the summary button or show a warning) for very large data is reasonable: “Please filter the data to under 100k rows to generate AI summary.” Alternatively, automatically switch to a different mode (like heavy sampling) and inform the user that the summary is based on partial data.
Indicate Scope of Summary: If the summary is based on a subset (sample, or just currently visible data, etc.), state that in the UI so the user knows how to interpret it. For example, prepend the summary text with a note like “(Summary below is based on a random sample of 1,000 rows)” or “(Summary of aggregated data by Department)”. This transparency builds trust; the user won’t think the AI “looked at everything” if it didn’t. It also educates them that for more accurate results, they might want to adjust the sample or criteria.
Loading and Streaming Feedback: Provide immediate feedback when the user requests a summary. A spinner or “Generating summary...” message should appear instantly. Because you plan to stream, you can then replace that with the streaming text. Ensure the container for the tooltip can update dynamically as new tokens arrive (the Vercel SDK will likely handle emitting events). If the summary is long, you might show partial results as bullets appearing one by one. Keep the tooltip open while streaming (if it’s a hover tooltip, you may want to make it click-activated for long content to avoid it disappearing mid-generation).
Limit the Length Appropriately: A summary for a huge dataset should still fit on a tooltip or side panel without overwhelming. It’s easy for an LLM to produce several paragraphs – but consider capping it (via prompt instructions like “at most 5 bullet points” or even using max_tokens in the API call). Users generally want a concise overview from a tooltip. If they want a full report, they likely would run a different tool. So aim for a few sentences to a short paragraph of output. If the data is pivoted or grouped, maybe a bullet per group highlight is okay, but don’t let it essay-out endlessly.
Error and Edge-case Messaging: If the AI summary can’t be produced (maybe the user is offline, or OpenAI is down, or data is just too large), have a friendly fallback message. For example, “Sorry, I can’t summarize this dataset (it might be too complex or large). Try filtering the data or check your connection.” This is better than a tooltip that appears blank or never shows up.
User Control: Provide a way for advanced users to customize the behavior. Perhaps an “AI Settings” panel where they can toggle “Use sample vs full data” or set the sample size, or choose which technique to prefer (though too many options might confuse non-technical users). At minimum, allow them to re-run the summary if they think it missed something – maybe a refresh button on the tooltip. You could also allow them to ask a follow-up question to the summary (if you have conversational capability) – e.g. “Why is department X growing?” – but that veers into a full chatbot feature beyond the immediate scope.
Visual Aids: Sometimes including small visuals can enhance a summary (though not asked, but consider for future): e.g. a tiny bar chart of category distribution that the AI or code generates, shown alongside the text. OpenAI function calling could even return a sparkline chart data which you render. This may be beyond what’s requested now, but it’s a UX idea to keep in mind, as summaries of data are often even better with visual context.
Testing with Users: Ensure the phrasing of summaries is understandable to the target audience. The AI might use terms like “mean” vs “average” or other jargon. In your prompt or post-processing, you can normalize these. Also ensure it doesn’t reveal any individual’s data if that’s sensitive – though if using aggregates, it shouldn’t.

By thoughtfully integrating these UX elements, you turn the AI summary from a black-box magic trick into an intelligible feature that users can trust and rely on. As a final example, Sigma Computing’s guidance suggests using filters to preview data before running LLM functions – similarly, guiding users to isolate what they want summarized will lead to better outcomes and a smoother experience.

Conclusion: Scaling an LLM summary tooltip to millions of rows is challenging but feasible with a combination of strategies. First, mitigate the risks (token limits, latency, cost) by reducing the data intelligently – through sampling, trimming, and pre-aggregating so the model only sees the most salient information. Use multi-step summarization (chunking or clustering) for truly huge datasets to break the task down, and consider advanced pipelines (like retrieval or function-calling) for the most robust long-term solution. Throughout, craft prompts that play to the model’s strengths, explicitly asking for the kind of summary you need. Architecturally, use the Vercel AI SDK’s streaming to keep latency low, and divide work between client (preprocessing) and server (AI calls) for efficiency. Finally, wrap the feature in a user experience that sets proper expectations – encouraging filtering on large data, indicating when summaries are approximate, and delivering the insights in a clear, concise format. By following these best practices, the AI summary tooltip can remain fast (<5s to first word), relevant, and helpful even as your data scales to the millions.

With these improvements, users will be able to interact with vast datasets and still quickly get the high-level story – fulfilling the promise of your organizational design SaaS to make sense of complex data through intelligent, context-aware summaries.

References:

Deepchecks Community – approaches to handle LLM token limits (truncation, sampling, chunking).
OpenAI Developer Forum – discussion noting LLMs struggle with raw numeric aggregation and the efficacy of letting tools/SQL handle data queries.
Quadratic Finance – integrating an LLM in spreadsheets and tips on prompting with specifics for data analysis.
Juice Analytics – step-by-step prompting method to summarize data, emphasizing structured guidance to ChatGPT.
LangChain documentation – Map-Reduce summarization for long texts, dividing into chunks and summarizing hierarchically.
Arize AI – workshop insights on better chunking (by topical clusters) to improve summarization quality.
Eagerworks (Juan P. Balarini) – “summarization through clustering” technique, selecting representative chunks from clusters to summarize large text efficiently.
Vishal Khare – Medium article on Centroid-Based Vector Selection for summarization, clustering and picking closest-to-centroid documents for LLM input.
Sigma Computing – best practices for using LLM functions in BI, recommending data cleaning, trimming to token limit, and filtering to limit rows before analysis.
Vercel AI SDK Guide – streaming responses to show output chunks as they arrive, improving perceived performance for users.

thomasdavis/large_data_ai_summaries.md