Overview
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.
Capabilities
Design Production Rag Architectures For Quality, Freshness, Security, Latency, And CostDebug Retrieval And Grounded-Generation Failures From Traces, Logs, Prompts, And EvalsDiagnose Parsing, Chunking, Indexing, Metadata, Filtering, Reranking, And Context IssuesEvaluate Retrieval Quality, Groundedness, Citation Correctness, Abstention, And RegressionsReview Prompt Injection, Rag Poisoning, Cross-Tenant Leakage, And Authorization RisksPlan Low-Blast-Radius Incident Containment, Verification, Rollback, And Permanent FixesImprove Hybrid Retrieval, Query Rewriting, Reranking, Context Packing, And Source PrecedenceDesign Observability For Retrieval Traces, Model Calls, Citations, Latency, Tokens, And CostCompare Retrievers, Rerankers, Models, Vector Stores, And Architecture Trade-OffsUse Current Official Documentation For Model, Vector-Store, Sdk, Security, And Platform Changes
Example Prompts
Click any prompt to copy it.
