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Build an evaluation set from support tickets
AI evaluation & quality control · Customer support · checked 2026-10-18
Use authorized, redacted ticket examples to define acceptable support answers. A reviewer checks the policy and expected answer for each case, then saves a versioned test file. Start with a manageable sample and add cases when real failures expose gaps.
Independent review. IonBin has no partnership with any tool on this page and was not paid for this page. Our evaluation is our own, based on public documentation and our research. Product names are trademarks of their owners and are used for identification only. How disclosure works.
Goal
Create reviewed test cases for a support assistant without treating past replies as automatically correct.
Connects through: Upload / export
Steps
01
Support lead
Select permitted examples and remove unnecessary personal or account information before sharing.
Human stepConfirmed
02
Reviewers
Write an expected answer or acceptance criteria; record the policy source and its version.
Human stepConfirmed
03
You
Keep one case per row in a Sheet or local file, with a stable ID and reviewer decision.
Store the approved cases and rubric in a restricted repository and review changes.
Human stepConfirmed
Confirmed means the vendor documents it (linked). It doesn't mean IonBin has tested it. AI-platform and human steps need no source.
Human approval
The support owner approves the cases, reference answers and access permissions before the test set is used.
Watch out for
A previous human reply can also be wrong. Check expected answers against the current policy.
Starter prompt
For these permitted, redacted support examples, propose acceptance criteria and questions for the policy owner. Cite the supplied policy for each criterion. Do not assume an old agent reply was correct; mark missing policy as unresolved.
Written for this workflow. Paste it into your AI platform and adjust it to your situation.
No platform-specific connection: the steps run in the tools themselves (browser, upload or built-in), with documented capabilities and human review steps. This is not an end-to-end test.
No platform-specific connection: the steps run in the tools themselves (browser, upload or built-in), with documented capabilities and human review steps. This is not an end-to-end test.
No platform-specific connection: the steps run in the tools themselves (browser, upload or built-in), with documented capabilities and human review steps. This is not an end-to-end test.
ChatGPT
Confirmed
No platform-specific connection: the steps run in the tools themselves (browser, upload or built-in), with documented capabilities and human review steps. This is not an end-to-end test.
Gemini
Confirmed
No platform-specific connection: the steps run in the tools themselves (browser, upload or built-in), with documented capabilities and human review steps. This is not an end-to-end test.
Perplexity
Confirmed
No platform-specific connection: the steps run in the tools themselves (browser, upload or built-in), with documented capabilities and human review steps. This is not an end-to-end test.
Readiness considers the connection evidence and unresolved steps. Confirmed: documented access and the proposed steps meet the recorded requirements. MCP and API pairings require an explicit reviewed platform note; a named plugin or CLI alone does not confirm MCP. Inferred: an explicit page note describes a plausible pairing that still needs testing. To verify: a connection or step is unresolved. An API and code execution alone do not establish compatible network access, credentials or permissions. None of this is IonBin testing.
What we checked
promptfoo documents CSV and other external test-case formats. Anthropic recommends explicit success criteria and examples that include difficult or ambiguous inputs. The sequence here is an IonBin implementation proposal; we have not run it against a real support system. Ticket extraction is a manual, permissioned input to this workflow, not a claimed help-desk integration.
Set it up
Define the repeated job narrowly: for example, draft a response explaining a return policy. Include cases where the assistant should ask for clarification or hand off.
Select examples across common requests, difficult cases and past failures. IonBin suggests 20 to 50 as an initial working sample, not evidence of statistical coverage.
Have the policy owner write or correct each reference answer. Multiple acceptable answers can share a rubric instead of an exact string.
Save stable case IDs, inputs, expected criteria, source version and reviewer decisions. Keep development cases separate from held-out cases reserved for final checks.
Map the file to the runner’s documented format and inspect the parsed cases before a full run.
The output is an evaluation dataset and rubric, not a trained model. Revisit the reference answers when policy changes, and keep confidential originals out of general-purpose test repositories.
Before relying on this workflow
The steps above are documentation-reviewed. Complete these checks in your own environment before treating the implementation as tested:
Pilot the CSV mapping with representative cases and inspect the parsed inputs and assertions.
Verify that redaction preserves the task while removing data the test environment should not receive.
Related agent skill
Once the eval set exists, Sentry’s Prompt Optimizer skill uses the same idea to improve a prompt: compare a few candidate rewrites on the cases and check the winner on held-out ones. IonBin reviewed it from source only.
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