archon-rag-specialist

What problem does it solve?

This Skill streamlines implementing, operating, and debugging retrieval-augmented generation (RAG) with Archon to produce relevant, attributed context for LLMs and reduce irrelevant or stale search results.

Core Features & Use Cases

• Indexing & Chunking: Best practices for chunk strategies, incremental and batch indexing, and parent document mapping to preserve provenance.
• Embeddings & Storage: Guidance on choosing embedding models, generating embeddings, and storing vectors in a vector database.
• Search, Filtering & Reranking: Vector, keyword, and hybrid search patterns, metadata filtering, reranking with cross-encoders, and deduplication.
• Context Building & Debugging: Techniques for dynamic context windows, multi-query RAG, scoring analysis, and diagnosing embedding or chunking quality issues.
• Use case: Index documentation and support articles to answer configuration questions with high-precision context for developer tools or support agents.

Quick Start

Use Archon to index your documentation with 500-character chunks, generate embeddings using a high-quality model, store vectors in your vector DB, and run a top-k vector search to retrieve context for a user query.