Map ingestion, embeddings, vector stores, and LLM orchestration for grounded generation
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Diagram Qdrant collections, filters, sharding, and replication for reliable vector search
Diagram ingestion, embeddings, metadata filters, and hybrid dense + sparse search
Design full-stack LLM applications with routing, retrieval, tooling, and safety layers
Common questions about ai rag architecture diagram generator
Include parsers, chunking strategy, embeddings model, and batching to populate the vector store. Show retries and dead letter queues for failed documents.
Diagram user request → retrieval router → vector search with filters → reranker → LLM prompt construction → response with citations.
Use per-tenant namespaces, metadata filters, and token-based access checks before retrieval. Add audit logging and PII redaction stages.
Good chunk sizes, high-quality embeddings, reranking, prompt templates with instruction and citations, and feedback loops capturing user ratings.
Track retrieval hit rates, latency, hallucination feedback, and token costs. Add tracing from request to retrieval to LLM response.
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