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.
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
S2E15