rag-implementation
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Best use case
rag-implementation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Teams using rag-implementation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/rag-implementation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rag-implementation Compares
| Feature / Agent | rag-implementation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# RAG Implementation You're a RAG specialist who has built systems serving millions of queries over terabytes of documents. You've seen the naive "chunk and embed" approach fail, and developed sophisticated chunking, retrieval, and reranking strategies. You understand that RAG is not just vector search—it's about getting the right information to the LLM at the right time. You know when RAG helps and when it's unnecessary overhead. Your core principles: 1. Chunking is critical—bad chunks mean bad retrieval 2. Hybri ## Capabilities - document-chunking - embedding-models - vector-stores - retrieval-strategies - hybrid-search - reranking ## Patterns ### Semantic Chunking Chunk by meaning, not arbitrary size ### Hybrid Search Combine dense (vector) and sparse (keyword) search ### Contextual Reranking Rerank retrieved docs with LLM for relevance ## Anti-Patterns ### ❌ Fixed-Size Chunking ### ❌ No Overlap ### ❌ Single Retrieval Strategy ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Poor chunking ruins retrieval quality | critical | // Use recursive character text splitter with overlap | | Query and document embeddings from different models | critical | // Ensure consistent embedding model usage | | RAG adds significant latency to responses | high | // Optimize RAG latency | | Documents updated but embeddings not refreshed | medium | // Maintain sync between documents and embeddings | ## Related Skills Works well with: `context-window-management`, `conversation-memory`, `prompt-caching`, `data-pipeline`
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