Your RAG system isn't hallucinating (It's worse)

Описание: Your RAG chatbot sounds helpful. It quotes your docs, answers fast, and looks professional in demos. But when real users show up with messy questions, it becomes confidently wrong — and you won't know until it damages trust.

This video reveals why retrieval-augmented generation systems fail in production even when they ace every demo. You'll learn the 3-layer diagnostic framework I use to identify exactly where chatbots break down.

What This Video Covers

If you've built a knowledge assistant, support chatbot, or document Q&A system using RAG architecture, and users complain the answers "sound good but aren't right", this video explains the root cause. Most teams spend 80% of their time on prompt engineering while ignoring retrieval quality and reasoning errors. That's backwards. I'll show you the three layers you need to diagnose:
Retrieval (did you get the right docs?)
Reasoning (did the model combine them correctly?)
Behavior (does it refuse when uncertain?)

Who This Is For

This is for ML engineers, product managers, and technical leaders building AI assistants on company documents. If you're connecting an LLM to internal knowledge bases, HR policies, product documentation, or support articles and you're frustrated by inconsistent or hallucinated responses, you need this framework. No code required. Just a mental model that changes how you debug.

Key Takeaways

Fluent text is not the same as reliable answers , your chatbot can sound articulate while being completely wrong
The 3-layer diagnostic model: Retrieval (right docs?), Reasoning (combined correctly?), Behavior (refuses when uncertain?)
Most hallucinations aren't the model "making things up", they're the system feeding weak or contradictory context and demanding an answer anyway
5 common mistakes: treating search results as truth, ignoring document contradictions, no fallback behavior, optimizing for demos instead of messy real prompts, shipping without monitoring
5 practical steps: define measurable success criteria, design question flow with refusal points, establish single source of truth, test with chaos, implement weekly failure reviews

CHAPTERS:
0:00 Your chatbot sounds smart until real users arrive
0:20 The "more docs + LLM" belief that fails
1:00 Fluent text vs. useful decisions
1:20 The 3-layer diagnostic framework
2:38 Real story: expense disaster from contradicting docs
3:52 5 mistakes I see constantly
4:37 Practical fixes (no tools required)
6:09 What's next: 4 failure categories

#RAG #RAGChatbot #LLM #AIEngineering #AIProduction #Hallucinations #RetrievalAugmentedGeneration #MLOps