Your support-ticket classifier works in a demonstration. Then your manager asks: “Can we switch to a cheaper AI model?”
You try five tickets. Both models give sensible answers. But what happens to an urgent customer problem buried among hundreds of routine requests?
To decide whether a cheaper AI model is good enough, compare it with your current system on the same representative inputs. Measure important errors, output validity and total operating cost. Choose a candidate using development data, then validate it on untouched test cases against requirements agreed in advance.
This guide explains that process through Snowflake’s AI evaluation previews and a support-ticket project. It is written for early-career engineers and analysts who can build a prototype but want stronger evidence for deployment decisions.
What did Snowflake announce about AI evaluation?
On September 21, 2026, Snowflake announced public previews of Cortex AI Function Evaluation and Cortex AI Function Optimization.12
Evaluation measures outputs against labeled datasets, collections of inputs with expected answers , and supports comparisons of prompts, models and function implementations. The announcement describes quality, cost and token-usage measurements. Tokens are the chunks of text processed by a language model.1
Optimization generates and evaluates alternative prompt/model configurations. Teams can compare quality and estimated cost, then create a reusable AI function from a selected candidate. The original function is not automatically replaced.2
My interpretation: this makes disciplined experimentation more accessible to teams already working in Snowflake. It does not establish that any particular model is reliable, or that a preview should immediately enter production.
What is AI model evaluation, and where does it fit in a system?
AI model evaluation is the systematic testing of a model’s outputs against defined requirements. For ticket routing, the requirements might include correct categories, reliable urgency detection and valid output formatting. A test case pairs an input with the behavior expected for that situation.
A grader is the rule that scores an output. Code can check whether a category matches the expected label. Open-ended answers may require human review or an LLM judge: another language model applying a scoring rubric. Such judges also need validation against human decisions.4
Snowflake exposes evaluation and optimization through experiments. Its documentation describes creating an experiment, executing it asynchronously on serverless compute, and inspecting run metrics and parameters. Snowflake manages the infrastructure; asynchronous execution lets the experiment run while you wait for results.3
In our proposed system, evaluation sits beside the live workflow. Before deployment, it tests a candidate against saved cases. The production path still needs ingestion, output validation, routing and operational monitoring. Measuring one classification function does not test all those components.
How can teams evaluate AI models without Snowflake?
Neither model comparison nor evaluation is new. A straightforward approach is a Python program that reads examples, calls each candidate, saves responses and calculates scores. A spreadsheet can support early manual review.
The engineering work includes preserving dataset versions, recording prompts, handling failed requests and making comparisons reproducible. A baseline : the current system or a simple alternative, gives improvements something meaningful to beat.
What does Snowflake’s integrated workflow improve?
Snowflake offers direct SQL interfaces and a guided AI Function Studio workflow over the same underlying capabilities.3 For a SQL-oriented team, that can reduce the separate tooling required to begin experimenting.
Its optimization feature can search candidate prompts and models using a consistent dataset and metric.2 That is useful when the question is specific: which tested configuration meets our requirements at an acceptable cost?
My assessment: the benefit is easier experiment management for teams already using Snowflake. The trade-off is dependence on platform-specific objects. Keep datasets and scoring rules portable. Optimization also needs a worthwhile objective: a search that maximizes overall accuracy can select a candidate that fails your most important cases.
Why can high AI accuracy hide critical failures?
The test can reward the wrong behavior. Imagine 950 routine tickets and 50 urgent ones. A model that marks everything routine achieves 95% urgency accuracy while finding none of the urgent cases. These numbers are illustrative.
Use several measures together:
MeasureQuestion it answersCategory accuracyWhat share of tickets received the correct category?Urgent recallWhat share of truly urgent tickets did we flag?Urgent precisionWhat share of flagged tickets were actually urgent?Invalid-output rateHow often was the response unusable by the next system?
Recall exposes missed urgent cases; precision exposes unnecessary escalations. Flagging everything maximizes recall while potentially overwhelming the support team.
Optimization can overfit. A configuration may become especially good at examples used to select it. Reserve a held-out test set that does not influence prompt changes or model selection. This applies the established principle of preventing test information from leaking into development.5
Estimated inference cost is incomplete. My recommended business calculation also considers retries, evaluation runs, infrastructure and human review. A cheaper answer can create more expensive follow-up work.
A function test has limited scope. It cannot establish that an entire agent correctly uses tools, respects permissions or completes a transaction. Agent evaluation must examine relevant outcomes, not merely plausible responses.4
Preview access has constraints. Snowflake documents availability in selected regions.3 Check your account, model access and current requirements before promising a tutorial will run everywhere.
Finally, do not introduce an LLM when a reliable rule solves the task. A known account-status code is usually better handled directly than interpreted through a prompt. Include that simpler baseline where applicable.
Example: should a support team switch to a cheaper model?
Consider a fictional software company testing ticket categorization and urgency detection. Its support lead defines urgent situations, reviews ambiguous examples and agrees on acceptable escalation workload.
Hypothetical comparison : not measured results or Snowflake benchmarks. Assume both candidates process the same tickets. Relative inference cost uses the current configuration as 1.00:
Candidate | Category accuracy|
Urgent recall|Urgent precision|Relative inference cost|
Current configuration|92%|96%|80%|1.00|
Cheaper configuration|94%|78%|90%|0.40|
In this hypothetical example, the cheaper candidate improves category accuracy and urgent precision, but its urgent recall falls from 96% to 78%. That means it misses a larger share of urgent tickets. If preserving urgent recall is a release requirement, this candidate fails despite its lower inference cost.
A possible next experiment is to use the cheaper model for categorization while retaining the existing urgency detector. That combination must be evaluated as a new configuration. It is a hypothesis, not a demonstrated solution.
How to build your first AI model evaluation project
Build a support-ticket model comparison. You need basic SQL or Python, CSV handling and an understanding of API requests. The deliverable is a reproducible result table and a short deployment recommendation.
Define the contract. Require a category from a fixed list and an urgency value. Write down what qualifies as urgent, including counterexamples. Validate the output structure before scoring its content.
Create 120 synthetic tickets. Include routine, urgent, ambiguous and incomplete messages. Manually review the labels. Split 80 into development data and 40 into a final test set, keeping near-duplicate messages together. Include urgent examples in both groups. With only 40 final cases, report error counts alongside percentages; a few outcomes can move the score substantially. Synthetic data supports practice, not claims about production reliability.
Establish a baseline. Run one prompt with one model. Save ticket ID, expected labels, predicted labels, raw output, model identifier, prompt version, run time and errors. Keep expected labels out of model inputs.
Compare deliberately. Change the prompt first, then compare models with that prompt fixed. Record configuration changes so you can explain what each experiment tested. Use only development data to select candidates.
Score the failures too. Report category accuracy, urgent recall, urgent precision, invalid-output rate and request failures. Do not silently drop failed requests. Repeat selected runs to see whether outcomes vary, while remembering that repeated runs do not create more independent examples.
Validate the decision on held-out data. Evaluate the baseline and selected candidate on the untouched set. Explain whether you would adopt, reject or investigate further. If you tune after seeing these results, you need fresh test data for another independent check.
Use Snowflake’s documented experiment workflow if your account supports it.3 Otherwise implement the same comparison in Python and clearly describe it as a platform-independent exercise. Publish your dataset, scoring rules, result table and failure analysis; do not invent results you have not measured.
Which engineering skills does this project demonstrate?
This project can demonstrate requirements analysis through precise labels, data engineering through versioned datasets, software engineering through validation and error handling, and experimental design through controlled comparisons.
What should you do before deploying the selected model?
Start by writing ten difficult examples and clear expected answers. Resolve disagreements before expanding the dataset. Then run a baseline and inspect every failure before adding optimization.
For production, repeat the evaluation on approved, representative data. Agree acceptance criteria with the workflow owner, test the complete routing process and monitor a limited rollout. Record how to restore the previous configuration. Re-evaluate when inputs, business rules or model versions change.
A defensible recommendation states the candidate, measured trade-offs, unresolved risks and rollout conditions. Learning to write that recommendation is as valuable as learning to run the model.
Sources
Product status checked September 23, 2026. Sources 1–3 support Snowflake feature claims; sources 4–5 support evaluation principles. The proposed architecture, project and fictional company are the author’s teaching examples.
Snowflake, Cortex AI Function Evaluation—Public Preview, September 21, 2026.
Snowflake, Cortex AI Function Optimization—Public Preview, September 21, 2026.
Snowflake, Cortex AI Function Evaluation and Optimization documentation, accessed September 23, 2026.
Anthropic, Demystifying evals for AI agents, January 9, 2026.
scikit-learn, Common pitfalls and recommended practices: data leakage, accessed September 23, 2026.