What is the primary benefit of enabling citations in Claude?

Enabling citations allows the AI to provide specific character-level references for its claims, making responses verifiable, reducing hallucinations, and increasing user trust.

How does contextual retrieval improve RAG performance?

Contextual retrieval involves prepending document-level context to individual chunks before embedding, which has been shown to improve retrieval accuracy by 49-67%.

Does this skill help reduce API costs for large documents?

Yes, it includes specific implementation guides for prompt caching, allowing you to cache large, static knowledge bases to reduce latency and costs on repeated queries.

Which citation formats are supported?

The skill includes helper functions to format extracted citations into common academic styles including APA, MLA, and Chicago, as well as numbered reference lists.

Can I use this skill to analyze multiple documents simultaneously?

Yes, the skill provides patterns for multi-document Q&A, allowing Claude to synthesize answers and cite sources across an entire collection of provided texts.

Document Citations & RAG

Name: Document Citations & RAG
Author: Lobbi-Docs

byLobbi-Docs

Ciencia de Datos y ML

Implements robust document attribution and Retrieval-Augmented Generation (RAG) patterns for verifiable AI responses.

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The Citations & Retrieval skill empowers developers to build trustworthy AI applications by grounding Claude's responses in specific source documents. It provides comprehensive implementation patterns for source attribution, character-level citation extraction, and advanced RAG pipelines. By utilizing techniques like contextual retrieval, which can improve accuracy by up to 67%, this skill ensures that AI-generated content is evidence-based and easily verifiable. It is essential for developers building research tools, technical documentation Q&A, or any system where accuracy and source transparency are paramount.

Características Principales

Advanced RAG pipeline implementation using vector embeddings
Contextual retrieval patterns for significantly higher accuracy
Automated citation formatting for APA, MLA, and Chicago styles
Prompt caching optimizations for high-performance document processing
0 GitHub stars
Source attribution with character-level and page-level precision

Casos de Uso

Building academic research and analysis tools with formal citations
Creating enterprise knowledge bases for technical or legal documentation
Developing grounded AI assistants that provide verifiable source links

Acerca de

Características Principales

Advanced RAG pipeline implementation using vector embeddings
Contextual retrieval patterns for significantly higher accuracy
Automated citation formatting for APA, MLA, and Chicago styles
Prompt caching optimizations for high-performance document processing
0 GitHub stars
Source attribution with character-level and page-level precision

Casos de Uso

Building academic research and analysis tools with formal citations
Creating enterprise knowledge bases for technical or legal documentation
Developing grounded AI assistants that provide verifiable source links