RAG & GenAI Copilot
Build and debug production RAG
Production RAG & GenAI Copilot helps you design, debug, evaluate, secure, and operate retrieval-augmented generation systems. It can trace failures across ingestion, parsing, chunking, indexing, retrieval, reranking, context construction, generation, citations, authorization, and evaluation; design practical RAG architectures; build retrieval and groundedness evals; review prompt-injection and data-leakage risks; and improve observability, latency, and cost. It favors measurable evidence, reversible fixes, source-level authorization, and simple architectures over prompt-only fixes or unnecessary complexity.
技能
資訊
- 功能
- Design production RAG architectures for quality, freshness, security, latency, and cost, Debug retrieval and grounded-generation failures from traces, logs, prompts, and evals, Diagnose parsing, chunking, indexing, metadata, filtering, reranking, and context issues, Evaluate retrieval quality, groundedness, citation correctness, abstention, and regressions, Review prompt injection, RAG poisoning, cross-tenant leakage, and authorization risks, Plan low-blast-radius incident containment, verification, rollback, and permanent fixes, Improve hybrid retrieval, query rewriting, reranking, context packing, and source precedence, Design observability for retrieval traces, model calls, citations, latency, tokens, and cost, Compare retrievers, rerankers, models, vector stores, and architecture trade-offs, Use current official documentation for model, vector-store, SDK, security, and platform changes
- 開發人員
- Krishna Sathvik
- 類別
- Developer Tools
- 版本
- 0.1.0