rag-architect

To design an end-to-end RAG pipeline, you need a complete architecture covering chunking strategies, embedding model selection, vector databases, retrieval methods, and reranking. It provides architecture diagrams, component recommendations, and cost projections tailored to your specific document types.

The best chunking strategy for technical documentation depends on your content structure. You can compare fixed-size, sentence-based, paragraph-based, or semantic heading-aware chunking to optimize retrieval accuracy and ensure the RAG system captures the context of your technical documents effectively.

To evaluate and optimize RAG retrieval accuracy and cost, implement an evaluation framework alongside cost projections. This involves selecting appropriate embedding models and vector databases, applying reranking methods, and monitoring production patterns to balance latency and cost requirements.

Yes, you can use this RAG pipeline design for code repositories and scientific knowledge bases. The architecture adapts to varied document types, ensuring the chunking, embedding, and retrieval methods suit domains like technical documentation, code, and science across small to large scale considerations.

For a production RAG pipeline, you need guardrails, monitoring patterns, and cost optimization configurations. The design provides configuration templates and architecture diagrams that account for production patterns to ensure scalable knowledge systems remain adaptable to future model updates.