rag-implementation▌
davila7/claude-code-templates · updated Apr 8, 2026
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.
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:
- Chunking is critical—bad chunks mean bad retrieval
- 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★26 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
rag-implementation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Amina Sethi· Dec 8, 2024
rag-implementation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakura Rahman· Dec 4, 2024
Keeps context tight: rag-implementation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Rahul Santra· Nov 27, 2024
We added rag-implementation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Diego Taylor· Nov 27, 2024
Solid pick for teams standardizing on skills: rag-implementation is focused, and the summary matches what you get after install.
- ★★★★★Michael Rahman· Nov 23, 2024
rag-implementation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Nov 19, 2024
I recommend rag-implementation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Pratham Ware· Oct 18, 2024
rag-implementation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Diego Liu· Oct 18, 2024
rag-implementation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ren Harris· Oct 14, 2024
Solid pick for teams standardizing on skills: rag-implementation is focused, and the summary matches what you get after install.
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