Editing Rag (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ RAG Architecture Variants
! Approach !! Description !! Best For
|-
| Naive RAG || Embed query → retrieve top-k → generate || Simple Q&A on clean documents
|-
| HyDE || Generate hypothetical answer → embed it → retrieve || Queries with technical jargon
|-
| Query expansion || Rewrite query multiple ways, retrieve for each, deduplicate || Ambiguous queries
|-
| Reranking || Use cross-encoder to reorder retrieved chunks || Precision-critical applications
|-
| Self-RAG || LLM decides whether to retrieve and critiques its own output || Multi-step reasoning
|-
| Corrective RAG || Evaluates retrieval quality; falls back to web search if low confidence || High-stakes factual queries
|}

'''Common failure modes:'''
* '''Retrieval failure''' — The relevant document wasn't retrieved (low recall). Causes: poor chunking, wrong embedding model, similarity metric mismatch.
* '''Context poisoning''' — Irrelevant chunks are included, confusing the LLM. Use reranking and confidence thresholds.
* '''Chunk boundary artifacts''' — Important context is split across chunks. Increase overlap or use semantic chunking.
* '''Prompt injection''' — Malicious content in retrieved documents instructs the LLM to behave differently. Sanitize inputs and use structured prompts.
* '''Stale index''' — The knowledge base isn't updated when documents change. Implement incremental indexing and deletion.
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