Evaluate Support Ticket Classifier
Runs support ticket test cases through the classifier to measure exact match accuracy against known labels.
developers and engineersops and IT teams
What it does
Measure your support ticket classifier's accuracy by running test cases against known labels to find exactly where it makes mistakes. You get precise performance scores using n8n and OpenAI Chat Model to validate how well the system predicts correct answers.
Step by step
- Configure a webhook trigger to receive test payloads containing ticket text and ground truth labels.
- Set up an OpenAI Chat Model node with a system prompt instructing it to output only the predicted category label.
- Connect the model output to an Evaluation Node that compares the prediction against the expected label field from the input data.
Where the LLM does the work
- Craft the system prompt for the OpenAI node to ensure consistent formatting and strict adherence to the defined classification schema.
- Define the specific evaluation logic in the Evaluation Node, such as whether to use exact string matching or fuzzy matching for category names.
- Design the final report structure that aggregates scores into a readable summary of precision and recall metrics.
Watch out for
The OpenAI model may output extra text like 'The answer is' which breaks exact match evaluation, so add a post-processing step to strip non-label characters.
Evaluation nodes in n8n can fail silently if input data types mismatch, so ensure the expected label field is always a string and not null.
Hardcoded category names in the prompt may drift from your actual database labels, so use dynamic variables to inject the current list of valid categories into the system prompt.
Tools that fit
OpenAI Chat Model LLM Classifying tickets by category and urgency
Evaluation Node Service Scoring AI predictions against expected labels
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Evaluate Support Ticket Classifier: Runs support ticket test cases through the classifier to measure exact match accuracy against known labels. ## Specification - What it does: Measure your support ticket classifier's accuracy by running test cases against known labels to find exactly where it makes mistakes. You get precise performance scores using n8n and OpenAI Chat Model to validate how well the system predicts correct answers. - Trigger: Run manually (Manual · on demand) - Autonomy: You stay in control - Expected setup effort: about an afternoon - Tools/services involved: - n8n: Workflow orchestration and evaluation trigger - OpenAI Chat Model: Classifying tickets by category and urgency - Evaluation Node: Scoring AI predictions against expected labels ## Known pitfalls, handle each one explicitly in your implementation 1. The OpenAI model may output extra text like 'The answer is' which breaks exact match evaluation, so add a post-processing step to strip non-label characters. 2. Evaluation nodes in n8n can fail silently if input data types mismatch, so ensure the expected label field is always a string and not null. 3. Hardcoded category names in the prompt may drift from your actual database labels, so use dynamic variables to inject the current list of valid categories into the system prompt. ## Reference implementation https://n8n.io/workflows/15133 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/evaluate-support-ticket-classifier/ via usecasesforagents.com
Frequently asked questions
Can I use a different Automation Platform than n8n?
Yes. n8n is only the example automation platform in this recipe. The same flow works with Home Assistant. Swap the automation platform connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for developers who want the busywork around code automated, not the code itself.
Ops-IT teams triaging their own internal requests get the same pattern, different queue.