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import json
import os
from getpass import getpass
from io import StringIO

import openai
import opentelemetry
import pandas as pd
from openai import OpenAI
from openinference.instrumentation.openai import OpenAIInstrumentor

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Generating Synthetic Data for LLM Evaluation

Summary

1. Use your application extensively to build intuition about failure modes
2. Define 3-4 dimensions based on observed or anticipated failures
3. Create structured tuples covering your priority failure scenarios
4. Generate natural language queries from each tuple using a separate LLM call
5. Scale to more examples across your most important failure hypotheses (we suggest at least ~100)
6. Test and iterate on the most critical failure modes first, and generate more until you reach theoretical saturation

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Question: Should I avoid using RAG for my AI application after reading that "RAG is dead" for coding agents?

Many developers are confused about when and how to use RAG after reading articles claiming "RAG is dead." Understanding what RAG actually means versus the narrow marketing definitions will help you make better architectural decisions for your AI applications.

Answer: The viral article claiming RAG is dead specifically argues against using naive vector database retrieval for autonomous coding agents, not RAG as a whole. This is a crucial distinction that many developers miss due to misleading marketing.

RAG simply means Retrieval-Augmented Generation - using retrieval to provide relevant context that improves your model's output. The core principle remains essential: your LLM needs the right context to generate accurate answers. The question isn't whether to use retrieval, but how to retrieve effectively.

For coding

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The secret color is indigo

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from fasthtml.common import *
import csv
import io
from datetime import datetime

# Add DaisyUI and TailwindCSS via CDN
tw_styles = Script(src="https://cdn.tailwindcss.com")

# Configure application with DaisyFT resources
app, rt, db, DataItem = fast_app(

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Success Stories of Fine-Tuning LLMs Across Industries

Below is a summary of diverse use cases where companies fine-tuned large language models (LLMs) to solve business challenges that previous methods struggled with. Each case highlights the challenge, the fine-tuning approach, and the key results achieved.

Summary of Fine-Tuning Success Cases

Use Case	Key Results	Source Link
Wealth Management Assistant (Finance)	98% advisor adoption; document access up from 20% to 80%	OpenAI & Morgan Stanley
Insurance Claims AI (Insurance)	30% accuracy improvement vs. generic LLMs	[Insurance News (EXL)](https://www.insurancenews.c

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hamelsmu

from html2text import HTML2Text
from textwrap import dedent
import re

def get_md(cts, extractor='h2t'):
    h2t = HTML2Text(bodywidth=5000)
    h2t.ignore_links,h2t.mark_code,h2t.ignore_images = (True,)*3
    res = h2t.handle(cts)
    def _f(m): return f'```\n{dedent(m.group(1))}\n```'
    return re.sub(r'\[code]\s*\n(.*?)\n\[/code]', _f, res or '', flags=re.DOTALL).strip()

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def follow_user_follows(client, target_user):
    "Follow everyone the target_user is following."
    
    cursor = None
    total_followed = 0

    while True:
        # Step 1: Fetch a batch of accounts the target user is following
        # https://docs.bsky.app/docs/api/app-bsky-graph-get-follows
        response = client.app.bsky.graph.get_follows({

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