A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
The idea here is different. Instead of just retrieving from raw documents at query time, the LLM incrementally builds and maintains a persistent wiki — a structured, interlinked collection of markdown files that sits between you and the raw sources. When you add a new source, the LLM doesn't just index it for later retrieval. It reads it, extracts the key information, and integrates it into the existing wiki — updating entity pages, revising topic summaries, noting where new data contradicts old claims, strengthening or challenging the evolving synthesis. The knowledge is compiled once and then kept current, not re-derived on every query.
This is the key difference: the wiki is a persistent, compounding artifact. The cross-references are already there. The contradictions have already been flagged. The synthesis already reflects everything you've read. The wiki keeps getting richer with every source you add and every question you ask.
You never (or rarely) write the wiki yourself — the LLM writes and maintains all of it. You're in charge of sourcing, exploration, and asking the right questions. The LLM does all the grunt work — the summarizing, cross-referencing, filing, and bookkeeping that makes a knowledge base actually useful over time. In practice, I have the LLM agent open on one side and Obsidian open on the other. The LLM makes edits based on our conversation, and I browse the results in real time — following links, checking the graph view, reading the updated pages. Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase.
This can apply to a lot of different contexts. A few examples:
- Personal: tracking your own goals, health, psychology, self-improvement — filing journal entries, articles, podcast notes, and building up a structured picture of yourself over time.
- Research: going deep on a topic over weeks or months — reading papers, articles, reports, and incrementally building a comprehensive wiki with an evolving thesis.
- Reading a book: filing each chapter as you go, building out pages for characters, themes, plot threads, and how they connect. By the end you have a rich companion wiki. Think of fan wikis like Tolkien Gateway — thousands of interlinked pages covering characters, places, events, languages, built by a community of volunteers over years. You could build something like that personally as you read, with the LLM doing all the cross-referencing and maintenance.
- Business/team: an internal wiki maintained by LLMs, fed by Slack threads, meeting transcripts, project documents, customer calls. Possibly with humans in the loop reviewing updates. The wiki stays current because the LLM does the maintenance that no one on the team wants to do.
- Competitive analysis, due diligence, trip planning, course notes, hobby deep-dives — anything where you're accumulating knowledge over time and want it organized rather than scattered.
There are three layers:
Raw sources — your curated collection of source documents. Articles, papers, images, data files. These are immutable — the LLM reads from them but never modifies them. This is your source of truth.
The wiki — a directory of LLM-generated markdown files. Summaries, entity pages, concept pages, comparisons, an overview, a synthesis. The LLM owns this layer entirely. It creates pages, updates them when new sources arrive, maintains cross-references, and keeps everything consistent. You read it; the LLM writes it.
The schema — a document (e.g. CLAUDE.md for Claude Code or AGENTS.md for Codex) that tells the LLM how the wiki is structured, what the conventions are, and what workflows to follow when ingesting sources, answering questions, or maintaining the wiki. This is the key configuration file — it's what makes the LLM a disciplined wiki maintainer rather than a generic chatbot. You and the LLM co-evolve this over time as you figure out what works for your domain.
Ingest. You drop a new source into the raw collection and tell the LLM to process it. An example flow: the LLM reads the source, discusses key takeaways with you, writes a summary page in the wiki, updates the index, updates relevant entity and concept pages across the wiki, and appends an entry to the log. A single source might touch 10-15 wiki pages. Personally I prefer to ingest sources one at a time and stay involved — I read the summaries, check the updates, and guide the LLM on what to emphasize. But you could also batch-ingest many sources at once with less supervision. It's up to you to develop the workflow that fits your style and document it in the schema for future sessions.
Query. You ask questions against the wiki. The LLM searches for relevant pages, reads them, and synthesizes an answer with citations. Answers can take different forms depending on the question — a markdown page, a comparison table, a slide deck (Marp), a chart (matplotlib), a canvas. The important insight: good answers can be filed back into the wiki as new pages. A comparison you asked for, an analysis, a connection you discovered — these are valuable and shouldn't disappear into chat history. This way your explorations compound in the knowledge base just like ingested sources do.
Lint. Periodically, ask the LLM to health-check the wiki. Look for: contradictions between pages, stale claims that newer sources have superseded, orphan pages with no inbound links, important concepts mentioned but lacking their own page, missing cross-references, data gaps that could be filled with a web search. The LLM is good at suggesting new questions to investigate and new sources to look for. This keeps the wiki healthy as it grows.
Two special files help the LLM (and you) navigate the wiki as it grows. They serve different purposes:
index.md is content-oriented. It's a catalog of everything in the wiki — each page listed with a link, a one-line summary, and optionally metadata like date or source count. Organized by category (entities, concepts, sources, etc.). The LLM updates it on every ingest. When answering a query, the LLM reads the index first to find relevant pages, then drills into them. This works surprisingly well at moderate scale (~100 sources, ~hundreds of pages) and avoids the need for embedding-based RAG infrastructure.
log.md is chronological. It's an append-only record of what happened and when — ingests, queries, lint passes. A useful tip: if each entry starts with a consistent prefix (e.g. ## [2026-04-02] ingest | Article Title), the log becomes parseable with simple unix tools — grep "^## \[" log.md | tail -5 gives you the last 5 entries. The log gives you a timeline of the wiki's evolution and helps the LLM understand what's been done recently.
At some point you may want to build small tools that help the LLM operate on the wiki more efficiently. A search engine over the wiki pages is the most obvious one — at small scale the index file is enough, but as the wiki grows you want proper search. qmd is a good option: it's a local search engine for markdown files with hybrid BM25/vector search and LLM re-ranking, all on-device. It has both a CLI (so the LLM can shell out to it) and an MCP server (so the LLM can use it as a native tool). You could also build something simpler yourself — the LLM can help you vibe-code a naive search script as the need arises.
- Obsidian Web Clipper is a browser extension that converts web articles to markdown. Very useful for quickly getting sources into your raw collection.
- Download images locally. In Obsidian Settings → Files and links, set "Attachment folder path" to a fixed directory (e.g.
raw/assets/). Then in Settings → Hotkeys, search for "Download" to find "Download attachments for current file" and bind it to a hotkey (e.g. Ctrl+Shift+D). After clipping an article, hit the hotkey and all images get downloaded to local disk. This is optional but useful — it lets the LLM view and reference images directly instead of relying on URLs that may break. Note that LLMs can't natively read markdown with inline images in one pass — the workaround is to have the LLM read the text first, then view some or all of the referenced images separately to gain additional context. It's a bit clunky but works well enough. - Obsidian's graph view is the best way to see the shape of your wiki — what's connected to what, which pages are hubs, which are orphans.
- Marp is a markdown-based slide deck format. Obsidian has a plugin for it. Useful for generating presentations directly from wiki content.
- Dataview is an Obsidian plugin that runs queries over page frontmatter. If your LLM adds YAML frontmatter to wiki pages (tags, dates, source counts), Dataview can generate dynamic tables and lists.
- The wiki is just a git repo of markdown files. You get version history, branching, and collaboration for free.
The tedious part of maintaining a knowledge base is not the reading or the thinking — it's the bookkeeping. Updating cross-references, keeping summaries current, noting when new data contradicts old claims, maintaining consistency across dozens of pages. Humans abandon wikis because the maintenance burden grows faster than the value. LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass. The wiki stays maintained because the cost of maintenance is near zero.
The human's job is to curate sources, direct the analysis, ask good questions, and think about what it all means. The LLM's job is everything else.
The idea is related in spirit to Vannevar Bush's Memex (1945) — a personal, curated knowledge store with associative trails between documents. Bush's vision was closer to this than to what the web became: private, actively curated, with the connections between documents as valuable as the documents themselves. The part he couldn't solve was who does the maintenance. The LLM handles that.
This document is intentionally abstract. It describes the idea, not a specific implementation. The exact directory structure, the schema conventions, the page formats, the tooling — all of that will depend on your domain, your preferences, and your LLM of choice. Everything mentioned above is optional and modular — pick what's useful, ignore what isn't. For example: your sources might be text-only, so you don't need image handling at all. Your wiki might be small enough that the index file is all you need, no search engine required. You might not care about slide decks and just want markdown pages. You might want a completely different set of output formats. The right way to use this is to share it with your LLM agent and work together to instantiate a version that fits your needs. The document's only job is to communicate the pattern. Your LLM can figure out the rest.
little QoL feature: Read less; get the meaning; move on
Description:
Add TL;DR to your ~/wiki. Caveman communication is the way <- Abstracting(less words; most meaning) the Answer/.md for the human. But you can click button to read details if you need to.
goal1: Read the gist of the ~/wiki or LLM answer. But you have option to read detailed answer. <- Abstracting the Answer/.md for the human. but you can click button to read details if you need
goal2: Read less words; get the the most meaning; decide to read the whole detailed .md or move on. The less you read the better; because you focus on output more -> writing. Always communicate(write, read in IDE) simply first, but you have option to go into detail(The main .md <- The source code; a very detailed almost research like .md paper or article for all context for LLM and analytical reading).
Solution:
https://github.com/JuliusBrussee/caveman
Call this summary version. or readable version. But the main .md This is for the LLM. While the other .md is the summary version. the TL;DR version that both are accessible to the same knowledge. This can be added in the editing layer where you ask Q&A as well.
Again the goal is less words; more meaning.
Details:
Problem1: The longer, the more /raw data you have. The more .md files you have to read. The longer it takes for you to read. The less your brain remembers. You keep asking the same Q&A again and again. Potential useful Q&A might not be asked. You miss understand the information contained in the ~/wiki. you dump bad /raw. LLM compiles. Asking Q to delete bad .md because of bad /raw. You waste time. LLM can't carry for everything.
Problem2: You read .md 1week ago. You don't really remember what it's all about. You ask Q&A, find it with llm help. You reread the .md that has the same detailed words. Your goal is only memory refresher not to re-read the whole thing again <-- too much scrolling down and too much eye scanning for many words. You take longer to kick the engine(producing actual human output) to feed /raw ingest.
idea1(something like this): Instead of LLM giving you 1 answer to your 1 question. It gives you 2 answers. You read the gist answer(very less words; keep most meaning) But you decide to read the fully detailed answer if you want.
Idea2(something like this): in compiling or producing .md. Make article1.md for LLM that is detailed. And make a 2nd version of the same article1-human.md file for the human to keep as much meaning as possible using as little words or data as possible. But user can decide to read further <-- saves time. Because there are always 2 files of the same .md. 1 for the LLM and your system 2 brain (long reading sessions or for reasoning) the other .md for your system 1 brain and long-term mental model(the logic) retention thought frequent repetition of the logic, because you read less to get the logic; build mental connections faster.
explaining:
There is a problem with the language used for edits. English or any LLM output language contains a lot of fluff that is baked in the Model's way of training -> Lot's of words; low meaning. This is not helpful when your goal is to look for personal information as effectively as possible.
This creates a problem where it gets harder to read after the 2k file in the wiki. <- It's a human problem. You solve it with Q&A sure. Don't read the ~/wiki just ask questions and LLM goes there and brings it up, gives links or sources at the very end.
I believe there should be 2 versions of .md of each file in the ~/wiki. One is the "detailed" or compiled .md output in all of it's glory, the source code .md. The 2nd is the same version but the focus is less words more meaning.
Where fast access is needed, fast communication. these ideas might help you over time to develop the gist in your brain just from the ~/wiki and the repeated process of fast Q&A and fast reading of the logic of .md first before going into detail. Your brain in theory should retain more of the answer so you don't have to ask the question that aren't needed. Also LLM is really good at compressing data into as little words as possible while preserving the highest meaning as possible.
Hopefully with less words you can work more efficiently as you specify or ask LLM for further questions to clarify. But first you need your brain to detect it first. Using big words when very few words get the job done saves brain power to focus on the words that matter the most.
Further abstractions are: Q&A but instead of getting a detailed answer or the option to pick the less wordy answer, you are provided with questions to which if answered you don't need to read.
So "A lot of details" --> "less words; preserve as much meaning" --> "1 or 2 questions or a series of questions to which answered by you in your brain then no need to read"