Advanced RAG Workflows

Lessons

1. 1Why retrieval failsThe problem is rarely the model.25m
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2. 2Chunking strategiesSemantic boundaries over fixed sizes.30m
3. 3Embeddings and hybrid searchCombine dense and keyword retrieval.30m
4. 4Evaluating a RAG systemMeasure retrieval and answer quality separately.25m
5. 5Cost, latency and cachingMaking it affordable to run.30m