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LLM Evaluation Metrics: Everything You Need for LLM Evaluation - Confident AI
This guide explains LLM evaluation metrics, including measures for retrieval augmented generation and agents, with DeepEval examples.
About LLM Evaluation Metrics: Everything You Need for LLM Evaluation - Confident AI
This Confident AI guide explains LLM evaluation metrics for scoring LLM system outputs against criteria such as correctness, relevance, hallucination, task completion, and tool use. It covers RAG metrics, AI agent metrics, multi-turn metrics, foundational model metrics, and use-case-specific metrics, and describes several methods for computing scores. The article includes working DeepEval code examples. It is a web guide rather than a standalone product, and no pricing details are provided.
Key features
- Explains LLM evaluation metrics for correctness, relevance, hallucination, task completion and tool use.
- Covers RAG metrics including faithfulness, answer relevancy, contextual precision, recall and relevancy.
- Covers AI agent metrics like task completion, tool correctness and plan quality.
- Describes statistical scorers, model-based scorers, G-Eval, DAG, Prometheus, QAG Score, GPTScore and SelfCheckGPT.
- Includes DeepEval code examples for implementing described LLM metrics.
- Distinguishes single-turn and multi-turn evaluation metrics for end-to-end or component-level systems.
Best for: Developers and teams building or evaluating LLM applications, RAG pipelines, or AI agents who need guidance on choosing and computing evaluation metrics.
Good for
- Covers a broad range of metric categories, including RAG, agent, multi-turn, foundational model and use-case-specific metrics.
- Provides working DeepEval code examples.
- Discusses common mistakes, such as relying on traditional scorers like BLEU and ROUGE.
Watch out for
- The guide states that generic metrics are not sufficient for specific use cases; at least one custom task-specific metric is recommended.
- It notes LLM evaluation remains difficult, especially deciding what to measure and how.
- No pricing details are provided.
LLM Evaluation Metrics: Everything You Need for LLM Evaluation - Confident AI at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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LLM Evaluation Metrics: Everything You Need for LLM Evaluation - Confident AI FAQ
What kinds of LLM evaluation metrics are covered?
RAG metrics, AI agent metrics, multi-turn metrics, foundational model metrics and use-case-specific metrics.
How does the guide describe computing metric scores?
It describes statistical scorers, model-based scorers, G-Eval, DAG, Prometheus, combining statistical and model-based scorers, QAG Score, GPTScore and SelfCheckGPT.
Does the guide include implementation examples?
Yes, it includes working DeepEval code examples.
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