Eliminate Hallucinations with Context Filter Guardrails
summary
Claims that context filter guardrails eliminate hallucination in RAG by scoring each retrieved chunk and dropping those below a threshold, using feedback functions (LLM-as-judge or small classifiers) to score relevance. Demo uses a vector store (chroma), OpenAI models, Grock and local Llama 3.1, a 5‑doc retrieval that filters three 0.2 scores leaving one 0.8 score. Latency examples: 2.3s with filtering vs 1.7s without, and filtered runs match expected answers.
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chapters (10)
- 0:00Introduction
- 1:07Guardrail inspiration
- 3:55How guardrails score work
- 7:25Setting up the demo environment
- 11:22Code implementation
- 13:28Filtering vs retrieval
- 15:15Model performance tests
- 16:59Open-source guardrails
- 18:24Tracing RAG execution
- 20:54Q&A
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