Implement and evaluate retrieval-augmented generation workflows with citation tracking.
npx skills add https://github.com/eco2-team/backend --skill rag-pipeline
Please help me install this Agent Skill. Skill: rag-pipeline Source: https://github.com/eco2-team/backend/tree/main/.claude/skills/rag-pipeline Command: npx skills add https://github.com/eco2-team/backend --skill rag-pipeline
RAG pipelines are complex to implement and evaluate; this guide provides a self-contained blueprint to build and test a Retrieval-Augmented Generation workflow, including context retrieval, evidence tracking, and phased evaluation.
High-intent search queries and answers about installing and using this skill.
To build a retrieval-augmented generation pipeline with evidence tracking, you need a guided framework that integrates contextual retrieval, citation generation, and phased evaluation to ensure grounded responses.
Phase 1-4 evaluation in retrieval-augmented generation is a structured testing framework that measures citation relevance, nugget completeness, groundedness, and next steps to validate answer accuracy.
Adding citation and evidence tracking to a RAG chatbot requires assembling retrieved context into grounded evidence references before generation, ensuring the output links back to source materials.
Yes, you can use local retrievers with a retrieval-augmented generation workflow by implementing modular integration with components like LocalAssetRetriever and TagBasedRetriever for contextual search.
The best way to evaluate groundedness in a RAG pipeline is applying a phased evaluation framework that specifically tests citation relevance and nugget completeness against the retrieved context.
Retrieval-augmented generation chatbots return ungrounded answers when lacking strict evidence tracking and phased evaluation, which are required to align generated responses with retrieved context.
Snowflake-LabsA concise summary of your roleplay character: name,</name> personality, and/or behavior, formatted as a single string sentence. Typically不超过50字，此处省略
Giskard-AIEvaluate RAG systems for groundedness, relevance, and citation accuracy.
fratilanicoConfigure dense, sparse, and hybrid document retrieval for RAG pipelines.
ramirlmProvides JSON search and browsing for customizable AI-powered web and product experiences.
