More reading
Why AI Projects Stall Before Production
The demo worked. Here is what actually blocks teams from getting it into production.
Eval Coverage Matters More Than Dataset Size
The issue is rarely too few eval rows. It is eval data that misses the real spread of cases, slices, and failure modes.
How DataFramer Measures Whether an AI Answer Is Right
In production there's no answer key. Here's how we calibrate LLM judges to your experts' definition of correct, and keep them honest.
Confidently Wrong AI Is a Business Problem
The dangerous AI mistakes aren't the obvious ones. They're fluent, confident, and wrong, and the business pays before engineering ever knows.
Benchmarking Coding Agents as Math Auditors: A Synthetic Financial Document Dataset
We built a financial benchmark with planted errors to test Claude Code as a math auditor — no manual labeling needed.
DataFramer vs Raw Claude: Long-Form Data Generation
Same LLM, dramatically different results. DataFramer vs raw Claude on 50K-token document generation.
100% Valid Text-to-SQL Data With Claude Haiku
From production SQL failure traces to 500 labeled, execution-validated eval samples - using only Claude Haiku.
How a 3B Model Beat GPT-4o on Hallucinations
A 3B open-source model beat GPT-4o at hallucination detection, built on purpose-built training and eval data.
Learning Center
Foundational reading on LLMs, RAG, and AI evaluationLLM-as-Judge: Why It's Hard to Get Right
When LLM-as-judge works, when it breaks down, and what it actually takes to build one you can trust.
DataFramer TeamTop Strategies for Detecting LLM Hallucinations
Ways to detect hallucinations in RAG and non-RAG systems, from rules and source checks to human review and calibrated judges.
DataFramer TeamTop RAG System Problems and How to Fix Them
The most common RAG failure modes and the best practices that address each one.
DataFramer TeamA Guide to Picking Your LLM Tech Stack
A practical breakdown of every layer in the LLM stack: models, orchestration, storage, and ops.
DataFramer TeamHow to Fix Hallucinations in RAG LLM Apps
Concrete techniques for diagnosing and reducing hallucinations in RAG-based LLM applications.
DataFramer TeamA Practical Guide to Agentic LLM Frameworks
A practical overview of agentic LLM frameworks: reasoning, planning, tool use, and the real challenges of running them in production.
DataFramer TeamComparing Vector Databases for RAG Systems
ApertureDB, Pinecone, Weaviate, and Milvus compared on features, performance, and RAG use cases.
DataFramer TeamRAG Explained: How It Works and Its Components
RAG components, retrieval strategies, and how to build systems that ground LLM outputs in real data.
DataFramer TeamSee it in action.