These optimizations reduce: KV Cache (Key-Value Cache) Problem Transformer attention recomputes attention matrices for all previous tokens during autoregressive generation. Without caching: Solution KV cache stores: for previously
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Step 1: Define Objective Examples: Key question: Step 2: Collect Data Sources: Step 3: Clean Data Remove: Step 4: Structure Dataset Formats: Example: { “instruction”: “Explain invoice
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Data Quality is Everything Garbage retrieval causes hallucinations. Critical issues: Document Parsing Challenges: Tools: Chunking Strategy One of the biggest RAG quality factors. Strategies: Metadata Engineering Essential metadata:
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Hallucination Types Type Example Fabrication Fake facts Citation hallucination Fake references Logical hallucination Broken reasoning Context hallucination Ignoring retrieved docs Root Causes Techniques to Reduce Hallucinations RAG Most
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What is RAG? Retrieval-Augmented Generation combines: to generate grounded responses. Complete RAG Stack User Query ↓ Query Understanding ↓ Embedding Generation ↓ Vector Retrieval
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