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Context & agent evals

Measure end-to-end quality.

Eval

Overview

Evals for LLM systems measure whether prompts and pipelines actually work, catching regressions as you iterate. This topic covers building test sets, scoring rubrics, and automating quality checks.

How it works

Eval
CasesRunScoreOutputDatasetsuiteExecutetraceRubricmetricsGate
ClientServiceEdgeData

Step by step, with examples

  1. 1

    Dataset

    • Representative task suite.
  2. 2

    Execute

    • Run the prompt/agent pipeline.
  3. 3

    Rubric

    • Success, cost, and latency.
  4. 4

    Gate

    • Block regressions in CI.
    • Example: eval-driven dev

Overview

Evaluate the whole pipeline: retrieval recall, grounding/faithfulness, task success, and cost/latency.

Key idea

Separate retrieval quality from generation quality so you know which half to fix.

Common pitfalls

  • Only eval'ing the final string
  • No retrieval metrics
  • Ignoring cost regressions

Where this content comes from

For full transparency, this content is curated and verified from these sources:

Frontier-lab prompting & agent guidesRetrieval-augmented generation literatureOppZen-authored context-engineering playbooks