RAG Pipeline Optimization with Claude
Design and optimize RAG systems for production-grade accuracy and performance
What You Can Do
You can systematically evaluate and improve your RAG pipelines using Claude as a design partner. You'll analyze retrieval quality, identify bottlenecks in your embedding and chunking strategies, and receive actionable recommendations to reduce hallucinations and improve context relevance. By the end, you'll have a data-driven optimization plan tailored to your specific use case and performance metrics.
Features
Analyze precision, recall, and relevance scores across your document chunks
Rewrite and decompose queries for better vector search results
Find the optimal granularity for your domain and retrieval patterns
Compare embedding strategies and select models for your performance needs
Identify gaps between retrieved context and model outputs, with concrete fixes
Design effective prompts that balance context length with accuracy
Evaluate semantic distance calculations and similarity thresholds
Craft system prompts and instructions that minimize out-of-context outputs
Example Output
RAG Evaluation Report
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✅ Retrieval Quality Score: 78/100
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Precision (correct docs returned): 82%
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Recall (relevant docs captured): 74%
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Mean reciprocal rank: 0.71
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❌ Key Issues Identified
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Chunk size (512 tokens) too small for multi-step reasoning queries
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Embedding model (text-embedding-3-small) loses semantic nuance for domain terminology
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Query rewriting not active; queries like "What does the warranty cover?" map to irrelevant sections
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💡 Optimization Plan
- Increase chunk size to 1024 tokens with 200-token overlap
- Migrate to text-embedding-3-large with domain-specific fine-tuning
- Add query decomposition: break multi-part questions into sub-queries
- Set similarity threshold to 0.75 (currently 0.5) to reduce noise
Expected Impact: Precision +12%, Recall +8%, hallucination rate -35%
What's Included
- SKILL.md: Full RAG optimization workflow with decision trees and evaluation frameworks
- RAG Quality Checklist: Evaluation criteria for retrieval, ranking, and context relevance
- Query Optimization Template: Strategies for decomposition, rewriting, and intent detection
- Embedding Strategy Decision Tree: Select models, chunk sizes, and vector similarity thresholds
- Performance Metrics Tracker: Template to log precision, recall, MRR, and latency across iterations
- Hallucination Root-Cause Analyzer: Worksheet to identify when and why the model deviates from context
Who It's For
- ML Engineers building and tuning RAG systems for production
- Data Engineers designing document ingestion and vector storage pipelines
- LLM Application Developers integrating retrieval into chat and search products
- AI Product Managers optimizing RAG performance for user-facing features
- Prompt Engineers evaluating context quality and reducing hallucinations
Best For
- Evaluating retrieval quality and identifying bottlenecks
- Optimizing chunk size, overlap, and embedding strategies
- Designing multi-turn RAG workflows with query rewriting
- Reducing hallucinations by improving context relevance
- Benchmarking RAG performance metrics (precision, recall, MRR)
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