Design and improve RAG systems, which retrieve documents to support language model answers. Explore meaningful document splitting, vector storage, combined keyword and semantic search, and relevance ranking while checking common causes of poor retrieval.
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Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
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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:
Chunk by meaning, not arbitrary size
Combine dense (vector) and sparse (keyword) search
Rerank retrieved docs with LLM for relevance
| 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 |
Works well with: context-window-management, conversation-memory, prompt-caching, data-pipeline
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Create interactive 3D scenes for websites, including product views and scroll-driven animations. Covers choosing Three.js, React Three Fiber, or Spline, preparing lightweight models, showing loading progress, and adapting experiences for mobile devices.
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Plan A/B tests that compare page or product changes, with clear hypotheses, sample sizes, success metrics, and traffic allocation. Get a structured test plan and guidance for interpreting results and deciding what to try next.
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Prepare an academic CV for faculty, postdoctoral, or research applications. Organize publications, grants, teaching, and research experience, with section order and formatting adapted to the role and academic field.
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Audit and improve web accessibility using WCAG 2.1 guidelines. Covers image descriptions, color contrast, keyboard controls, forms, and screen reader support, with code examples and automated and manual checks to guide fixes.
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Audit websites for accessibility barriers and improve image descriptions, color contrast, forms, keyboard navigation, and screen reader support. Provides code examples and automated and manual testing guidance for work toward WCAG 2.1 AA or AAA accessibility standards.
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Design autonomous agents for Claude Code plugins, including when they should run, what tools they may use, and how they should respond. Provides configuration templates, prompt guidance, and validation steps for creating agent files.
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