rag-implementation
Build Retrieval-Augmented Generation systems with vector databases and semantic search.
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for rag-implementation, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology — every score is re-derived by the same automated method as an agent's public evidence changes.
Overview
An Agent Skill that guides the agent to build RAG pipelines grounding LLM output in external knowledge via vector DBs and semantic search. It covers chunking, embedding, retrieval, and citation to reduce hallucination. The skill injects architecture and code patterns for document Q&A and doc assistants.
Key features and capabilities
- Vector-DB retrieval and semantic search
- Hallucination reduction via grounding
- Source-citation and chunking patterns
Use cases
- Building document Q&A systems
- Grounding LLMs in proprietary knowledge