llm-rag-engineer
CommunityBuild robust RAG systems with evaluation.
Authorthesammykins
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Retrieval-Augmented Generation pipelines often struggle with hallucinations, misalignment between retrieved content and answers, and a lack of robust evaluation.
Core Features & Use Cases
- Design RAG pipelines (Ingestion -> Chunking -> Embedding -> Retrieval -> Generation) to orchestrate data flow.
- Implement advanced retrieval techniques (Hybrid Search, HyDE expansion, and cross-encoder re-ranking) to boost relevance and faithfulness.
- Set up evaluation frameworks (Ragas, Arize Phoenix) to quantify hallucination, context fidelity, and uncertainty.
- Enforce prompt-security best practices to mitigate prompt injection and data leakage.
Quick Start
Configure an end-to-end RAG workflow with evaluation, HyDE expansion, and prompt-security guardrails for a given document store.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
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Please help me install this Skill: Name: llm-rag-engineer Download link: https://github.com/thesammykins/dotfiles/archive/main.zip#llm-rag-engineer Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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