Extract semantic metadata from Skill Units for RAG indexing.
npx skills add https://github.com/sammcj/agentic-coding --skill deepeval
Please help me install this Agent Skill. Skill: deepeval Source: https://github.com/sammcj/agentic-coding/tree/main/Claude/skills/deepeval Command: npx skills add https://github.com/sammcj/agentic-coding --skill deepeval
This Skill provides comprehensive evaluation capabilities for LLM applications, ensuring reliability and performance across RAG systems, conversational AI, and agent workflows.
Use the deepeval skill to evaluate the response quality of your customer support chatbot against common queries and edge cases.
High-intent search queries and answers about installing and using this skill.
LLM evaluation measures response quality, safety, and adherence to requirements across retrieval-augmented generation and conversational systems. Deepeval provides 50+ metrics to automatically test response quality, role adherence, and safety against diverse scenarios without manual inspection.
Yes, deepeval integrates with pytest to run evaluation metrics as automated tests. This enables component-level tracing via decorators to evaluate individual LLM system parts within your existing test pipeline.
RAG evaluation combines retrieval correctness with generation quality across your full pipeline. Deepeval's metrics cover RAG-specific scenarios—context relevance, answer correctness, and hallucination detection—enabling precise measurement of end-to-end system performance.
Safety evaluation detects harmful outputs, bias, and policy violations in conversational AI. Deepeval includes safety metrics to automatically flag problematic responses and ensure your chatbot meets compliance and ethical standards.
Yes, deepeval supports async evaluation to handle high-throughput scenarios efficiently. Component-level tracing with decorators works across async functions, enabling performance measurement at scale without blocking your application.
Deepeval supports custom evaluation metrics alongside its 50+ built-in ones, allowing you to define domain-specific criteria. This flexibility lets you measure LLM outputs against your unique business logic, compliance rules, or application-specific quality standards.
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