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Great question! Let me break down RAG (Retrieval-Augmented Generation) and its best practices.

RAG combines the power of large language models with external knowledge retrieval, creating systems that can access up-to-date information beyond their training data.

How RAG Pipelines Work

1. Document Ingestion — Source documents are chunked into manageable pieces (typically 256-1024 tokens) and converted into vector embeddings.

2. Vector Storage — Embeddings are stored in a vector database (Pinecone, Weaviate, Qdrant, or pgvector) with metadata for filtering.

3. Query Processing — When a user query arrives, it’s embedded using the same model, then the database performs a similarity search to find the top-k most relevant chunks.

4. Context Augmentation — The retrieved chunks are injected into the LLM prompt as additional context, grounding the response in factual data.

5. Generation — The LLM generates a response conditioned on both the query and the retrieved context.

Best Practices

• Chunk overlap: Use 10-20% overlap between chunks to avoid losing context at boundaries
• Hybrid search: Combine dense vector search with sparse (BM25) retrieval for better recall
• Reranking: Apply a cross-encoder reranker on top-k results before passing to the LLM
• Metadata filtering: Leverage document metadata to narrow retrieval scope

from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = Pinecone.from_documents(docs, embeddings, index_name="nexus-rag")

results = vectorstore.similarity_search(
    query="How does attention work?",
    k=5,
    filter={"source": "research_papers"}
)

RAG dramatically reduces hallucination and enables domain-specific AI without fine-tuning.