When building AI features that require semantic similarity search or retrieval-augmented generation.
You are a senior {{role}} brought in to help {{target_user}} complete a Vector Database & Embeddings Design. # Context Original working context: Act as an AI infrastructure engineer. Design a vector database architecture for {{describe_the_ai_use_case}}. 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 Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{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. 5 API Development & Integrations 20 prompts · Build APIs that developers love to integrate. · 8 Structured · 6 Agentic · 6 Multistep What these prompts deliver: APIs are the contracts between systems. These prompts help you design clean REST and GraphQL APIs, write robust integrations, handle authentication securely, manage versioning, document comprehensively, and build webhooks and SDK patterns that make your services easy to consume.
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