Solutions
Model evaluation, adaptation and deployment
Measure a model on your task first, improve it the cheapest way that works, then serve it.
The problem
Teams pick a model from a leaderboard or a vendor page and discover later that it does not do their task reliably, or costs more than expected. Fine-tuning is often suggested before anyone has measured a baseline.
What it looks like
Generic examples of the kind of work. They are not client results.
Which model for this task?
The same set of your own examples is run through candidate models under recorded conditions, and the results are compared.
Improving results without training
Better instructions, structured output or retrieval from your documents, measured against the baseline.
Serving a chosen model
Hosting a model you have decided to run yourself, with monitoring and a plan for updates.
What you receive
- An evaluation set built from your real or clearly synthetic examples, with the scoring rules written down
- A baseline measurement of one or more models on that set
- A recommendation that separates what was measured from what is opinion
- Prompt, retrieval or output-format changes, re-measured against the baseline
- Fine-tuning only where measurement shows it is justified, and the evidence for that
- A serving setup and documentation if you decide to host a model
The pilot and how we judge it
One task, one evaluation set and a small number of candidate approaches.
Acceptance criteria, written before the pilot starts:
- The evaluation set and scoring are agreed before any model is run.
- Every approach is run under the same recorded conditions.
- Results are reported as measured, including where they are poor.
- The decision about tuning or hosting follows from the numbers, not from a preference.
Boundaries and honest limits
- Provider features, model names and prices change. Results are dated and describe the conditions that were tested.
- A standard virtual server is not a guaranteed way to serve a large model. We check the hardware requirement first.
- Support for a language such as Arabic or Darija is tested on your task rather than inferred from a vendor's language list.
- We do not promise that tuning will beat a well-prepared baseline.
One question
Which manual process currently takes the most time from your team?
Answer in a sentence or two. An engineer replies with where we would start, what it takes and what we would not automate.