Build production retrieval-augmented generation systems combining vector databases, embeddings, and LLMs.
Convert text into dense vector representations for semantic search.
Store and query embeddings with Pinecone, pgvector, or Qdrant.
Split documents intelligently to maximise retrieval accuracy.
Tune top-k, reranking, and MMR for higher-quality context.
Combine dense and sparse retrieval for robust, production-grade search.
Score and compare prompts systematically to find what actually works.
Package your RAG pipeline for scale with observability and caching.
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