ai-llm-rag-engineering
CommunityBuild advanced RAG systems for grounded LLM responses.
Software Engineering#vector database#LLM#RAG#hybrid search#chunking#reranking#retrieval-augmented generation
Authorvasilyu1983
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Static RAG systems often suffer from irrelevant retrieval, hallucinations, and poor context grounding. This Skill provides practical, production-grade RAG design patterns with modern advances for dynamic and intelligent retrieval.
Core Features & Use Cases
- Advanced Retrieval: Implement hybrid retrieval (BM25 + vector) with cross-encoder reranking and Reciprocal Rank Fusion (RRF) for significant relevance gains.
- Optimal Chunking: Utilize page-level or semantic chunking strategies for highest accuracy and recall, tailored to document structure.
- Grounded Generation: Design contextual retrieval, context compression, and citation patterns to minimize hallucinations and ensure factual accuracy in LLM responses.
Quick Start
Use the ai-llm-rag-engineering skill to design a chunking strategy for a PDF document knowledge base, aiming for high accuracy and recall.
Dependency Matrix
Required Modules
None requiredComponents
referencesassets
💻 Claude Code Installation
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Please help me install this Skill: Name: ai-llm-rag-engineering Download link: https://github.com/vasilyu1983/AI-Agents-public/archive/main.zip#ai-llm-rag-engineering Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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