How do I evaluate an LLM application?
Build a small set of real failure cases before reaching for a framework. Twenty examples drawn from actual production mistakes catch more regressions than a thousand synthetic ones, and you can read the results.
Surveyed 17 September 2026
Evaluate an LLM application
Open in Tools →- 01Langfuse
Open-source LLM engineering platform with tracing, prompt management, evaluations, and cost monitoring for AI apps.
Developer Tools - 02LangSmith
Tracing, evaluation, and monitoring for LLM apps, framework-agnostic and usable without LangChain.
Developer Tools - 03Ragas
Evaluation framework for RAG pipelines scoring retrieval and generation on faithfulness and context precision.
Developer Tools - 04OpenAI Evals
Framework from OpenAI for evaluating LLM systems, with an open registry of benchmarks the community can extend.
Developer Tools - 05Anthropic Evals
Public suite of reference tasks and frameworks for benchmarking Claude and other language models.
AI Tools - 06Scenario
Agent testing framework using LLM-simulated users to run end-to-end conversations against any agent framework.
Developer Tools - 07OpenJudge
Evaluation framework that scores LLM quality with reward models and grader skills for RLHF and agent alignment.
AI Tools - 08UltraEval
Open source framework that evaluates foundation models through modular benchmarks for capability assessment.
AI Tools
A curated selection in editorial order. Use the fit and evidence to judge it for your task. Something missing?
What to look for
- 01Does it support both deterministic checks and model-graded scoring? You need the first wherever it is possible.
- 02Can it run in CI on every change, or only as a manual batch?
- 03Does it version the test set with the prompt? A score is meaningless without knowing what produced it.
Common questions
- How many examples does an eval set need?
- Start at twenty drawn from real failures. Small and real beats large and synthetic — you can actually inspect the results, which is where the useful signal is early on.
- Is using a model as a judge reliable?
- For relative comparisons and clear rubrics, reasonably. It is biased toward length and its own style, so calibrate against human labels before trusting the absolute number.
More in Understand the AI landscape
- Find AI benchmarksBrowse the evaluations used to compare models and agents — coding, tool use, cost, and the domain-specific long tail.
- Observe an LLM applicationTrace calls, inspect failures, and monitor latency, quality, and spend.
- Choose an inference providerCompare model routers, inference clouds, cloud catalogs, and direct lab APIs without collapsing them into one category.
- Run models locallyUse local inference runtimes and model managers on your own hardware.
- Add vector searchStore embeddings and retrieve relevant context for AI applications.