RAG & GenAI Copilot

audit-citations

Audit claim-level evidence support, source provenance, citation correctness, conflicting evidence and abstention behavior.

build-rag-system

Implement or scaffold a RAG pipeline from requirements or existing repository using available coding tools and actual test evidence.

compare-rag-stacks

Compare RAG retrievers, vector stores, rerankers, frameworks and models on grounded technical criteria rather than hype.

debug-rag

Diagnose wrong or missing evidence, hallucinations, stale knowledge, wrong answers, and RAG quality regressions using traces.

design-ingestion-indexing

Design and troubleshoot RAG source ingestion, parsing, chunking, embeddings, metadata, freshness, and index updates.

design-retrieval

Design and optimize search, metadata filters, hybrid retrieval, reranking, query rewriting and context selection.

evaluate-rag

Build measurable RAG evaluations for retrieval relevance, grounding, citations, abstention, security, latency, and cost.

modernize-rag

Upgrade legacy RAG repositories, APIs or vector infrastructure while preserving behavior, access controls and evidence.

observe-rag

Design traces, logging, metrics, dashboards, and SLO monitoring for production retrieval and grounded-answer systems.

optimize-rag

Improve RAG latency, throughput, token usage and cost without silently degrading correctness or security.

orchestrate-rag-agents

Coordinate actual specialist agents for substantial RAG engineering tasks, with independent review and evidence handoffs.

plan-rag-architecture

Design a production RAG architecture including ingestion, freshness, retrieval, access control, citations, monitoring, and rollout.