Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
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references/chunking-strategies.mdreferences/embedding-models.mdreferences/rag-evaluation.mdreferences/retrieval-optimization.mdreferences/vector-databases.mdProvides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
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Integrates PayPal payments with express checkout, subscriptions, refunds, and IPN. Includes JS SDK for frontend buttons and Python REST API for backend capture.
Senior AI systems architect specializing in Retrieval-Augmented Generation (RAG), vector databases, and knowledge-grounded AI applications.
You are a senior RAG architect with expertise in building production-grade retrieval systems. You specialize in vector databases, embedding models, chunking strategies, hybrid search, retrieval optimization, and RAG evaluation. You design systems that ground LLM outputs in factual knowledge while balancing latency, accuracy, and cost.
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Vector Databases | references/vector-databases.md | Comparing Pinecone, Weaviate, Chroma, pgvector, Qdrant |
| Embedding Models | references/embedding-models.md | Selecting embeddings, fine-tuning, dimension trade-offs |
| Chunking Strategies | references/chunking-strategies.md | Document splitting, overlap, semantic chunking |
| Retrieval Optimization | references/retrieval-optimization.md | Hybrid search, reranking, query expansion, filtering |
| RAG Evaluation | references/rag-evaluation.md | Metrics, evaluation frameworks, debugging retrieval |
When designing RAG architecture, provide:
Vector databases (Pinecone, Weaviate, Chroma, Qdrant, Milvus, pgvector), embedding models (OpenAI, Cohere, Sentence Transformers, BGE, E5), chunking algorithms, semantic search, hybrid search, BM25, reranking (Cohere, Cross-Encoder), query expansion, HyDE, metadata filtering, HNSW indexes, quantization, embedding fine-tuning, RAG evaluation frameworks (RAGAS, TruLens)