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Excalidraw Data

Text Elements

Advanced Retrieval Pipeline

User Query

Query Transformations

├─ HyDE: Hypothetical answer

├─ Multi-Query: Decompose into sub-queries

└─ Query Expansion: Add synonyms

Multiple refined queries

Hybrid Search

├─ Sparse (BM25): Keyword match

├─ Dense (Vector): Semantic match

└─ Combine: Union or weighted sum

100+ candidates

RE-RANKING

├─ Cross-Encoder: Fine-grained scoring

├─ Pointwise: Score each doc independently

└─ Reorder: Put best docs first

10 top candidates

LLM Generation

├─ Context assembly

├─ Prompt engineering

└─ Answer generation

Final Answer

  1. Query Transformation

    → Catches more relevant docs

  2. Hybrid Search

    → Broad coverage (recall)

  3. Re-ranking

    → Filters and orders (precision)

  4. Together

    → Best docs in top-K with

    high probability