Documentation quality is the silent killer of RAG systems. A single ambiguous sentence might corrupt an entire set of responses. But the hardest part isn't fixing errors - it's finding them.
Today we are talking to Max Buckley on how to find and fix these errors.
Max works at Google and has built a lot of interesting experiments with LLMs on using them to improve knowledge bases for generation.
We talk about identifying ambiguities, fixing errors, creating improvement loops in the documents and a lot more.
Some Insights:
- A single ambiguous sentence can systematically corrupt an entire knowledge base's responses. Fixing these "documentation poisons" often requires minimal changes but identifying them is challenging.
- Large organizations develop their own linguistic ecosystems that evolve over time. This creates unique challenges for both embedding models and retrieval systems that need to bridge external and internal vocabularies.
- Multiple feedback loops are crucial - expert testing, user feedback, and system monitoring each catch different types of issues.
Max Buckley: (All opinions are his own and not of Google)
Nicolay Gerold:
00:00 Understanding LLM Hallucinations 00:02 Challenges with Temporal Inconsistencies 00:43 Issues with Document Structure and Terminology 01:05 Introduction to Retrieval Augmented Generation (RAG) 01:49 Interview with Max Buckley 02:27 Anthropic's Approach to Document Chunking 02:55 Contextualizing Chunks for Better Retrieval 06:29 Challenges in Chunking and Search 07:35 LLMs in Internal Knowledge Management 08:45 Identifying and Fixing Documentation Errors 10:58 Using LLMs for Error Detection 15:35 Improving Documentation with User Feedback 24:42 Running Processes on Retrieved Context 25:19 Challenges of Terminology Consistency 26:07 Handling Definitions and Glossaries 30:10 Addressing Context Misinterpretation 31:13 Improving Documentation Quality 36:00 Future of AI and Search Technologies 42:29 Ensuring Documentation Readiness for AI
