RAG Implementer

What problem does it solves? This Skill addresses the challenge of making Large Language Models (LLMs) reliable and factual by grounding their responses in up-to-date, domain-specific, or proprietary data, thereby reducing hallucinations and providing source attribution.

Core Features & Use Cases

• Knowledge Base Design: Guides through data source mapping, intelligent chunking strategies, and metadata enrichment for effective retrieval.
• Retrieval Pipeline: Implements advanced techniques like hybrid search, re-ranking, and query enhancement to fetch the most relevant information.
• Evaluation & Monitoring: Establishes metrics for retrieval and generation quality, ensuring production-ready performance and continuous improvement.

Quick Start

Fastest path to RAG:

1. Define knowledge scope
   • Identify data sources
   • Choose chunking strategy (500-1000 tokens)
   • Add metadata for filtering
2. Choose embedding model
   • General: text-embedding-3-large (OpenAI)
   • Code: code-search-babbage-code-001
   • Test on sample queries
3. Set up vector store
   • Managed: Pinecone
   • Self-hosted: Weaviate or Qdrant
   • Lightweight: Chroma or pgvector