---
id: llm-evaluation-opik-traces-datasets-and-experiments
title: 'LLM Evaluation Opik Traces, Datasets, and Experiments'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-11'
created_date: '2026-06-11'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-llm-evaluation-opik-traces-datasets-and-experiments-1
    statement: >-
      Opik evaluation documentation says evaluating an LLM application can give
      confidence in the performance of the application.
    source_title: Opik Evaluate Your Agent
    source_url: https://www.comet.com/docs/opik/evaluation/evaluate_your_llm
    confidence: medium
  - id: fact-ai-llm-evaluation-opik-traces-datasets-and-experiments-2
    statement: >-
      Opik evaluation documentation describes an evaluation flow that includes a
      dataset, metrics, and an evaluation experiment.
    source_title: Opik Evaluate Your Agent
    source_url: https://www.comet.com/docs/opik/evaluation/evaluate_your_llm
    confidence: medium
  - id: fact-ai-llm-evaluation-opik-traces-datasets-and-experiments-3
    statement: >-
      Opik evaluation documentation says adding tracking gives visibility into
      each evaluation run.
    source_title: Opik Evaluate Your Agent
    source_url: https://www.comet.com/docs/opik/evaluation/evaluate_your_llm
    confidence: medium
  - id: fact-ai-llm-evaluation-opik-traces-datasets-and-experiments-4
    statement: >-
      Opik observability documentation describes tracing as a way to log traces
      for LLM applications.
    source_title: Opik Observability Overview
    source_url: https://www.comet.com/docs/opik/tracing/overview
    confidence: medium
  - id: fact-ai-llm-evaluation-opik-traces-datasets-and-experiments-5
    statement: >-
      Opik evaluation documentation says built-in evaluation metrics include
      deterministic heuristic metrics and LLM-as-a-judge metrics.
    source_title: Opik Evaluate Your Agent
    source_url: https://www.comet.com/docs/opik/evaluation/evaluate_your_llm
    confidence: medium
completeness: 0.82
known_gaps:
  - Opik evaluation evidence depends on dataset version, task function, trace instrumentation, scorer configuration, model settings, sampling, prompt version, experiment filters, and whether production traces match the offline evaluation population.
disputed_statements: []
primary_sources:
  - title: Opik Evaluate Your Agent
    type: documentation
    year: 2026
    url: https://www.comet.com/docs/opik/evaluation/evaluate_your_llm
    institution: Comet
  - title: Opik Observability Overview
    type: documentation
    year: 2026
    url: https://www.comet.com/docs/opik/tracing/overview
    institution: Comet
secondary_sources: []
updated: '2026-06-11'
ai_models:
  - gpt-5-codex
---

## TL;DR

Opik traces, datasets, and experiments give agents a concrete audit trail for why an LLM or agent evaluation passed, failed, or drifted.

## Core Explanation

LLM evaluation needs reproducible inputs and observable execution. Opik can connect a dataset item, evaluation task, metric output, and trace hierarchy, which helps agents debug whether a failure came from retrieval, prompt behavior, tool use, or scorer expectations.

Agents should preserve dataset name, item IDs, experiment ID, trace ID, task code version, prompt version, model settings, scorer names, metric thresholds, and sampled outputs. Without this metadata, evaluation results are hard to compare across runs or map back to production behavior.

## Source-Mapped Facts

- Opik evaluation documentation says evaluating an LLM application can give confidence in the performance of the application. ([source](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm))
- Opik evaluation documentation describes an evaluation flow that includes a dataset, metrics, and an evaluation experiment. ([source](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm))
- Opik evaluation documentation says adding tracking gives visibility into each evaluation run. ([source](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm))
- Opik observability documentation describes tracing as a way to log traces for LLM applications. ([source](https://www.comet.com/docs/opik/tracing/overview))
- Opik evaluation documentation says built-in evaluation metrics include deterministic heuristic metrics and LLM-as-a-judge metrics. ([source](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm))

## Further Reading

- [Opik Evaluate Your Agent](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm)
- [Opik Observability Overview](https://www.comet.com/docs/opik/tracing/overview)