[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CVCY2THTCTETVTWRBTVZKG":3,"skill-same:01M6CVCY2THTCTETVTWRBTVZKG":69},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":51,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":53,"updatedAt":54,"install":55,"resources":68},"01M6CVCY2THTCTETVTWRBTVZKG","rag-implementation",null,"RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Frag-implementation\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39],"rag","rag implementation","implementation","personal","safe","workflow","setup","database","vector","Optimization","vector database","implementation workflow","selection","2026-11-10 10:12:30",false,"---\nname: rag-implementation\ndescription: \"RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.\"\ncategory: granular-workflow-bundle\nrisk: safe\nsource: personal\ndate_added: \"2026-02-27\"\n---\n\n# RAG Implementation Workflow\n\n## Overview\n\nSpecialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.\n\n## When to Use This Workflow\n\nUse this workflow when:\n- Building RAG-powered applications\n- Implementing semantic search\n- Creating knowledge-grounded AI\n- Setting up document Q&A systems\n- Optimizing retrieval quality\n\n## Workflow Phases\n\n### Phase 1: Requirements Analysis\n\n#### Skills to Invoke\n- `ai-product` - AI product design\n- `rag-engineer` - RAG engineering\n\n#### Actions\n1. Define use case\n2. Identify data sources\n3. Set accuracy requirements\n4. Determine latency targets\n5. Plan evaluation metrics\n\n#### Copy-Paste Prompts\n```\nUse @ai-product to define RAG application requirements\n```\n\n### Phase 2: Embedding Selection\n\n#### Skills to Invoke\n- `embedding-strategies` - Embedding selection\n- `rag-engineer` - RAG patterns\n\n#### Actions\n1. Evaluate embedding models\n2. Test domain relevance\n3. Measure embedding quality\n4. Consider cost\u002Flatency\n5. Select model\n\n#### Copy-Paste Prompts\n```\nUse @embedding-strategies to select optimal embedding model\n```\n\n### Phase 3: Vector Database Setup\n\n#### Skills to Invoke\n- `vector-database-engineer` - Vector DB\n- `similarity-search-patterns` - Similarity search\n\n#### Actions\n1. Choose vector database\n2. Design schema\n3. Configure indexes\n4. Set up connection\n5. Test queries\n\n#### Copy-Paste Prompts\n```\nUse @vector-database-engineer to set up vector database\n```\n\n### Phase 4: Chunking Strategy\n\n#### Skills to Invoke\n- `rag-engineer` - Chunking strategies\n- `rag-implementation` - RAG implementation\n\n#### Actions\n1. Choose chunk size\n2. Implement chunking\n3. Add overlap handling\n4. Create metadata\n5. Test retrieval quality\n\n#### Copy-Paste Prompts\n```\nUse @rag-engineer to implement chunking strategy\n```\n\n### Phase 5: Retrieval Implementation\n\n#### Skills to Invoke\n- `similarity-search-patterns` - Similarity search\n- `hybrid-search-implementation` - Hybrid search\n\n#### Actions\n1. Implement vector search\n2. Add keyword search\n3. Configure hybrid search\n4. Set up reranking\n5. Optimize latency\n\n#### Copy-Paste Prompts\n```\nUse @similarity-search-patterns to implement retrieval\n```\n\n```\nUse @hybrid-search-implementation to add hybrid search\n```\n\n### Phase 6: LLM Integration\n\n#### Skills to Invoke\n- `llm-application-dev-ai-assistant` - LLM integration\n- `llm-application-dev-prompt-optimize` - Prompt optimization\n\n#### Actions\n1. Select LLM provider\n2. Design prompt template\n3. Implement context injection\n4. Add citation handling\n5. Test generation quality\n\n#### Copy-Paste Prompts\n```\nUse @llm-application-dev-ai-assistant to integrate LLM\n```\n\n### Phase 7: Caching\n\n#### Skills to Invoke\n- `prompt-caching` - Prompt caching\n- `rag-engineer` - RAG optimization\n\n#### Actions\n1. Implement response caching\n2. Set up embedding cache\n3. Configure TTL\n4. Add cache invalidation\n5. Monitor hit rates\n\n#### Copy-Paste Prompts\n```\nUse @prompt-caching to implement RAG caching\n```\n\n### Phase 8: Evaluation\n\n#### Skills to Invoke\n- `llm-evaluation` - LLM evaluation\n- `evaluation` - AI evaluation\n\n#### Actions\n1. Define evaluation metrics\n2. Create test dataset\n3. Measure retrieval accuracy\n4. Evaluate generation quality\n5. Iterate on improvements\n\n#### Copy-Paste Prompts\n```\nUse @llm-evaluation to evaluate RAG system\n```\n\n## RAG Architecture\n\n```\nUser Query -> Embedding -> Vector Search -> Retrieved Docs -> LLM -> Response\n                |              |              |              |\n            Model         Vector DB     Chunk Store    Prompt + Context\n```\n\n## Quality Gates\n\n- [ ] Embedding model selected\n- [ ] Vector DB configured\n- [ ] Chunking implemented\n- [ ] Retrieval working\n- [ ] LLM integrated\n- [ ] Evaluation passing\n\n## Related Workflow Bundles\n\n- `ai-ml` - AI\u002FML development\n- `ai-agent-development` - AI agents\n- `database` - Vector databases\n","# RAG Implementation Workflow\n\n## Overview\n\nSpecialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.\n\n## When to Use This Workflow\n\nUse this workflow when:\n- Building RAG-powered applications\n- Implementing semantic search\n- Creating knowledge-grounded AI\n- Setting up document Q&A systems\n- Optimizing retrieval quality\n\n## Workflow Phases\n\n### Phase 1: Requirements Analysis\n\n#### Skills to Invoke\n- `ai-product` - AI product design\n- `rag-engineer` - RAG engineering\n\n#### Actions\n1. Define use case\n2. Identify data sources\n3. Set accuracy requirements\n4. Determine latency targets\n5. Plan evaluation metrics\n\n#### Copy-Paste Prompts\n```\nUse @ai-product to define RAG application requirements\n```\n\n### Phase 2: Embedding Selection\n\n#### Skills to Invoke\n- `embedding-strategies` - Embedding selection\n- `rag-engineer` - RAG patterns\n\n#### Actions\n1. Evaluate embedding models\n2. Test domain relevance\n3. Measure embedding quality\n4. Consider cost\u002Flatency\n5. Select model\n\n#### Copy-Paste Prompts\n```\nUse @embedding-strategies to select optimal embedding model\n```\n\n### Phase 3: Vector Database Setup\n\n#### Skills to Invoke\n- `vector-database-engineer` - Vector DB\n- `similarity-search-patterns` - Similarity search\n\n#### Actions\n1. Choose vector database\n2. Design schema\n3. Configure indexes\n4. Set up connection\n5. Test queries\n\n#### Copy-Paste Prompts\n```\nUse @vector-database-engineer to set up vector database\n```\n\n### Phase 4: Chunking Strategy\n\n#### Skills to Invoke\n- `rag-engineer` - Chunking strategies\n- `rag-implementation` - RAG implementation\n\n#### Actions\n1. Choose chunk size\n2. Implement chunking\n3. Add overlap handling\n4. Create metadata\n5. Test retrieval quality\n\n#### Copy-Paste Prompts\n```\nUse @rag-engineer to implement chunking strategy\n```\n\n### Phase 5: Retrieval Implementation\n\n#### Skills to Invoke\n- `similarity-search-patterns` - Similarity search\n- `hybrid-search-implementation` - Hybrid search\n\n#### Actions\n1. Implement vector search\n2. Add keyword search\n3. Configure hybrid search\n4. Set up reranking\n5. Optimize latency\n\n#### Copy-Paste Prompts\n```\nUse @similarity-search-patterns to implement retrieval\n```\n\n```\nUse @hybrid-search-implementation to add hybrid search\n```\n\n### Phase 6: LLM Integration\n\n#### Skills to Invoke\n- `llm-application-dev-ai-assistant` - LLM integration\n- `llm-application-dev-prompt-optimize` - Prompt optimization\n\n#### Actions\n1. Select LLM provider\n2. Design prompt template\n3. Implement context injection\n4. Add citation handling\n5. Test generation quality\n\n#### Copy-Paste Prompts\n```\nUse @llm-application-dev-ai-assistant to integrate LLM\n```\n\n### Phase 7: Caching\n\n#### Skills to Invoke\n- `prompt-caching` - Prompt caching\n- `rag-engineer` - RAG optimization\n\n#### Actions\n1. Implement response caching\n2. Set up embedding cache\n3. Configure TTL\n4. Add cache invalidation\n5. Monitor hit rates\n\n#### Copy-Paste Prompts\n```\nUse @prompt-caching to implement RAG caching\n```\n\n### Phase 8: Evaluation\n\n#### Skills to Invoke\n- `llm-evaluation` - LLM evaluation\n- `evaluation` - AI evaluation\n\n#### Actions\n1. Define evaluation metrics\n2. Create test dataset\n3. Measure retrieval accuracy\n4. Evaluate generation quality\n5. 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