RAG & GenAI Copilot
ChatGPT pluginBuild 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.
- Featured
- No
not on the featured shelf
Movement
From our daily snapshots over the last 30 days.
Rank by placement
Rank in Developer tools
In its category
How this plugin compares with others in the same category. Positions are counted within the category, not across the whole directory.
Developer tools492nd of 742 by placement
| # | Plugin | Position | 7d |
|---|---|---|---|
| 490 | #5432 | 73places up | |
| 491 | #5441 | 57places down | |
| 492 | #5448 | 94places down | |
| 493 | #5465 | 73places down | |
| 494 | #5467 | 52places up |
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
- Sponsorship
- Promote this plugin