LLM Evaluation Plan Template

Short answer: an LLM evaluation plan defines what good behavior means, which failures are unacceptable, what examples prove it, which metrics and human reviews are used, and which release gates block risky model, prompt, RAG, or agent changes.

Use this template after comparing LLM evaluation tools, AI agent testing frameworks, prompt evaluation tools, and RAG evaluation tools.

Evaluation Plan Template

Section	What to define	Evidence
Use case	Workflow, users, expected outcome, and operating context.	Use-case brief and example tasks.
Failure modes	Incorrect answer, unsupported claim, bad retrieval, unsafe tool use, policy breach, bad formatting, cost spike, latency failure.	Failure taxonomy and severity rubric.
Golden dataset	Representative examples, edge cases, expected answers, source material, and reviewer labels.	Dataset with owners and refresh cadence.
Eval methods	Deterministic checks, LLM judges, human review, RAG metrics, task completion checks, and policy tests.	Eval suite and scoring rules.
Release gates	Which evals block production, trigger review, or only inform rollout.	Release threshold and change log.
Monitoring loop	How production traces, user feedback, and incidents become new eval examples.	Trace sampling plan and feedback workflow.

Failure-Mode Register

Failure mode	Severity	Eval type	Owner
Answer contradicts approved source material.	High	Faithfulness and citation review.	AI/data owner
RAG system retrieves stale or irrelevant context.	High	Retrieval precision, recall, and human source review.	Data owner
Agent calls a tool without required approval.	Critical	Permission and state-change test.	Engineering owner
Prompt change improves tone but reduces factual accuracy.	Medium	Regression eval and reviewer comparison.	Product owner
Output is correct but unusable for the workflow.	Medium	Task acceptance review.	Business owner
Cost or latency exceeds workflow budget.	Medium	Operational threshold check.	Engineering owner

Golden Dataset Plan

Collect 30 to 100 real examples before optimizing prompts or models.
Include easy cases, edge cases, high-risk cases, and known failure cases.
Store source material, expected answer, unacceptable answer patterns, and reviewer notes.
Label examples by workflow, risk tier, data source, user type, and failure mode.
Refresh the dataset with production failures, escalations, and newly launched workflows.

Release Gate

Gate	Pass rule	Action if failed
Critical policy tests	100% pass on restricted actions, permissions, and unsafe output checks.	Block release.
Core task success	Meets agreed pass threshold on representative examples.	Fix prompt, context, model, or tool flow before release.
RAG grounding	Answers are supported by retrieved context for high-risk queries.	Fix retrieval, source freshness, or answer rules.
Regression suite	No material drop from the prior accepted version.	Review diff and decide whether the tradeoff is intentional.
Human acceptance	Reviewer accepts outputs for the workflow, not just the text.	Convert reviewer objections into new eval examples.

Weekly Eval Loop

Sample production traces and user feedback.
Tag failures by failure mode and severity.
Add high-signal failures to the golden dataset.
Run the eval suite against the current production version and proposed changes.
Ship only when quality, cost, latency, and policy gates are acceptable.
Review score movement with business and engineering owners.

Sources

How To Keep The Plan Alive

An eval plan should change every time the agent fails in production, a business rule changes, or a new user group gets access. Keep one owner for the eval dataset, require examples for every serious defect, and review failed cases weekly until the failure mode is either fixed, accepted, or blocked by product scope. The template is only useful if it becomes part of release management.

Related Brainforge Resources

Brainforge POV: eval plans are not scorecards for demos. They are the operating system for changing AI systems safely: examples, traces, human review, release gates, and weekly improvement loops.