Evaluation Concepts

Opik provides two complementary approaches to evaluating your LLM application. Understanding when to use each will help you build a robust evaluation strategy.

Test Suites — assertion-based testing

Test Suites let you define expected behaviors as natural-language assertions. An LLM judge checks each assertion against your agent's output and reports pass/fail results.

Best for:

• Testing specific behaviors (e.g., "the response does not hallucinate")
• Pass/fail validation of agent outputs
• Iterating on prompts and comparing versions
• Catching regressions after changes

A Test Suite has three main components:

1. Test items: Input data for your agent (e.g., questions with context, user scenarios)
2. Assertions: Natural-language descriptions of expected behavior, checked by an LLM judge (e.g., "The response is concise")
3. Execution policy: Controls how many times each item is run and how many runs must pass

Assertions can be defined at two levels:

• Suite-level assertions apply to every test item
• Item-level assertions apply only to a specific test item, in addition to suite-level ones

Pass/fail logic

• A run passes if all its assertions pass
• An item passes if the number of passed runs meets the pass_threshold
• The pass rate is the ratio of passed items to total items

Datasets & Metrics — quantitative scoring

Dataset-based evaluation scores your agent's outputs using quantitative metrics. You define a dataset of test cases, run your agent against them, and score the results using pre-built or custom metrics.

Best for:

• Measuring quality across many traces with a common metric (hallucination, relevance, coherence)
• Comparing model or prompt versions with numeric scores
• Evaluating RAG pipelines with context precision/recall metrics
• Building leaderboards across experiments

A dataset-based evaluation has three main components:

1. Dataset: A collection of test cases with inputs and optional expected outputs
2. Task: A function that takes a dataset item and returns your agent's output
3. Metrics: Scoring functions that evaluate the output (e.g., Hallucination, AnswerRelevance, custom metrics)

Each evaluation run creates an Experiment — a record of every dataset item, your agent's output, and the metric scores. Experiments are stored in Opik so you can compare them side-by-side.

Choosing between the two

	Test Suites	Datasets & Metrics
Output	Pass/fail per assertion	Numeric scores per metric
Evaluation method	LLM judge checks natural-language assertions	Scoring functions (LLM-based or heuristic)
Best for	Behavioral testing, regression checks	Quality measurement, benchmarking
Iteration style	Update assertions, re-run suite	Update dataset or metrics, re-run experiment

You can use both approaches together. For example, use Test Suites during development to validate specific behaviors, and Datasets & Metrics in CI to track quality scores over time.