RAG Pipeline Builder
İleridataEn az 32K bağlam
Designs and implements retrieval-augmented generation (RAG) pipelines end to end. Covers document chunking strategies, embedding model selection, vector store configuration, hybrid and re-ranking retrieval, prompt construction with grounded citations, and evaluation harnesses for measuring retrieval quality and answer faithfulness.
Kullanım alanları
- Building production RAG over internal documentation
- Choosing chunking and embedding strategies for a corpus
- Adding hybrid search and re-ranking to improve recall
- Designing citation-grounded answer prompts
- Creating retrieval and faithfulness evaluation suites
Örnek prompt
Design a RAG pipeline for a customer support knowledge base of ~50,000 markdown articles. Requirements: - Sub-second retrieval latency - Answers must cite source articles - Multilingual content (English, Spanish, German) Provide: 1. Chunking strategy with rationale 2. Embedding model recommendation and vector store choice 3. Hybrid retrieval + re-ranking design 4. Prompt template enforcing grounded citations 5. Evaluation plan (retrieval recall, answer faithfulness) 6. Example implementation code
Önerilen modeller
Uyumlu araçlar
claude-codecursorkiroany
Modaliteler
Giriş: text, code
→Çıkış: text, code
İlgili Skills
Yazar
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