The best model for your activity is one that succeeds in your tasks with an acceptable level of quality, time and cost. A general classification does not replace a essay on your documents and constraints. First prepare a supplier-independent comparison grid.
Define a fairly specific task
“Helping marketing” is too wide. Prefer “produce a first article plan from an interview” or “sort requests into existing categories”. Describe authorized entries, expected result and what requires human validation. Choose a perimeter where you can check an error without depending on just another model.
Prepare representative cases
Create a small set of ordinary files, ambiguous cases and situations that cannot be solved with the information provided. Use appropriate test data, without transmitting confidential documents to an invalid service. Keep a reference or success criteria for each case. Separate the examples used to adjust the setpoint from those used to compare the results.
Define what makes a result acceptable
To prepare site content, an outing can be well written while distorting the offer. Your grid should therefore distinguish between blocking requirements and style preferences. Define these requirements before seeing the model responses. Otherwise, it becomes easy to adapt the rule to the most attractive result and to produce a comparison that does not repeat itself.
| Criterion | Verification | decision if the criterion fails |
|---|---|---|
| fidelity to the material provided | No services or references added | Refuse or correct it before use |
| Accuracy of facts | Each important statement can be justified | Return to source document |
| Respect of the perimeter | The text responds to the requested task | Review the instruction or configuration |
| reading quality | The person concerned understands the next step | Revise the plan and formulations |
| recovery effort | The necessary human time is recorded | Integrate this effort into the cost of the course |
For a message classification, use expected categories, a category of doubt and a list of important confusions instead. For an extraction, check the missing fields, units and invented values. You do not need an identical grid for all tasks. The Hugging Face — RAG evaluation can help organize measures, but do not define for you what suits your activity.
Measure useful quality
Assess the accuracy, compliance with instructions, quality of French and the need for correction. For an extraction, check the fields and omissions. For a draft, check the facts and fidelity to the starting material. The evaluation tools presented by Hugging Face can organize measures, but the choice of criterion remains linked to your task and its consequences.
Fictitious example: ranking requests
A team compares two configurations on the same fictional messages. One responds faster, but confuses maintenance requests and urgent incidents. The decision can favor a category "to read again" rather than a forced classification. The human recovery time must be counted: a cheap exit that requires a long correction is not necessarily economical in the full course.
Calculate a labor cost, not just a generation cost
The useful cost of a trial includes preparation, generation, verification, correction and integration into the site. If you quantify it, use the rates and times actually applicable at the time of the test. Do not present an estimate as a measured bill. A configuration can produce quickly and then ask for a lot of recovery because it mishandles the exceptions in your documentation.
- Provide the same material and the same setpoint to the compared configurations. Keep versions and settings that influence the result.
- Present the outputs to the reviewer without announcing the configuration used, when possible. This method limits the effect of expectations on judgment.
- Note blocking errors separately from time and style. A favorable average should not mask a category of dangerously poorly processed requests.
- Keep a few new cases for the end of the trial. If the configuration is only successful for the examples already worked on, the test has not yet demonstrated its ability to help on a daily basis.
The final choice can remain conditional: a tool to prepare a plan to read again, another for a structured extraction, and no automation for a decision outside the scope. A small site does not necessarily need a new built-in template for each task. One Brief from an expert interview and better organized documentation can improve the outcome before any technical change. Replay the acceptance test when the version of the model or process evolves.
decide then monitor
Document Version, Settings, Setpoint and Test Date. Compare total working time, access limits, integration, and data processing conditions. Plan a non-regression test when changing. Do not turn your sample results into universal superiority. For a documentary application, the Quality of source research deserves a measure distinct from that of the model.