When building AI features that require semantic similarity search or retrieval-augmented generation.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Databases & Data Engineering - Use case: Vector Database & Embeddings Design - Source task: - Design a vector database architecture for {{describe_the_ai_use_case_semantic_search_rag_recommendation_syst}}. Include: - 1. embedding model selection (OpenAI, Cohere, sentence-transformers : choose for the use case) - 2. vector database choice (Pinecone, Weaviate, Qdrant, pgvector : with rationale) - 3. chunking strategy for documents (how to split text before embedding) - 4. indexing and query design (ANN algorithm choice) - 5. metadata filtering design to enable hybrid search # Goal Embedding model recommendation, vector DB choice, chunking strategy, ANN indexing design, and metadata filtering for hybrid search. # Constraints - Produce a complete, usable first draft in one response. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output Embedding model recommendation, vector DB choice, chunking strategy, ANN indexing design, and metadata filtering for hybrid search.
{{double-curly}} with your real context.When building AI features that require semantic similarity search or retrieval-augmented generation.
Chunking strategy has more impact on RAG quality than the choice of embedding model — experiment with chunk sizes and overlap before optimising the model.
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