Vector Databases and Enterprise RAG Systems
Embeddings and Similarity Search
Vector databases store document embeddings and enable fast similarity search. When a user asks a question, the system retrieves the most relevant chunks and passes them to an LLM for grounded, accurate answers.
Choose embedding models that match your domain. Generic models work for general knowledge; fine-tuned or domain-specific embeddings improve accuracy for specialized content.
RAG Architecture and Best Practices
Effective RAG requires chunking strategy, retrieval tuning, and prompt design. Chunk size affects recall — smaller chunks are precise but may miss context; larger chunks provide context but dilute relevance.
Implement hybrid search when possible: combine vector similarity with keyword and metadata filters. Add reranking for top-k results to improve precision.
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