RAG & GenAI Copilot
ChatGPT pluginBuild and debug production RAG
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Developer tools478th of 537 by placement
| # | Plugin | Position | 7d |
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| 475 | #5331 | — | |
| 476 | #5332 | — | |
| 477 | #5333 | — | |
| 478 | #5338 | — | |
| 479 | #5351 | — | |
| 480 | #5356 | — | |
| 481 | #5369 | — |
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.
Example prompts
- “Debug why this RAG system is returning the wrong evidence.”
- “Design a production RAG architecture for this use case.”
- “Build an eval plan for retrieval, grounding, citations, and abstention.”
Skills
production-rag-genai-copilot
Production-focused workflow for designing, debugging, evaluating, securing, and operating RAG and grounded GenAI systems across ingestion, retrieval, reranking, context, citations, observability, latency, and cost.
- Categories
- Made by
- Krishna Sathvik
- Sign-in
- On Install
- Capabilities
- 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
- Availability
- Available
- Version
- 0.1.0
- Keywords
- rag, genai, retrieval, llm, evaluation, security, observability
- Created
- September 23, 2026