Editing Retrieval-Augmented Generation (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a production RAG system:

'''1. Document pipeline architecture'''
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Document Sources (PDFs, web, DB, APIs)
    ↓
[Ingestion service: parse, clean, deduplicate]
    ↓
[Chunking: semantic or recursive, 500-1000 tokens]
    ↓
[Embedding model: text-embedding-3-small or BGE-M3]
    ↓
[Vector store: Pinecone / Weaviate / pgvector]
    ↓
[Metadata index: BM25 for hybrid search]
</syntaxhighlight>

'''2. Query pipeline architecture'''
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User Query
    ↓
[Query rewriting / expansion]
    ↓
[Hybrid retrieval: semantic + BM25 keyword]
    ↓
[Reranking: cross-encoder or Cohere Rerank API]
    ↓
[Context assembly: top 5–10 chunks + metadata]
    ↓
[Prompt construction: system + context + query]
    ↓
[LLM generation with citation extraction]
    ↓
[Post-processing: source validation, confidence scoring]
    ↓
Response + Citations
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'''3. Key design decisions'''
* Embedding model: balance quality vs. cost (BGE-M3 is strong open-source; text-embedding-3 for OpenAI ecosystem)
* Chunk size: 512–1024 tokens; larger for narrative text, smaller for dense technical docs
* Hybrid search (semantic + BM25) outperforms either alone on most benchmarks
* Always return source citations to build user trust and enable verification

[[Category:Artificial Intelligence]]
[[Category:Large Language Models]]
[[Category:Natural Language Processing]]
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