How to choose an AI model for your product
Turn a crowded model shortlist into a practical decision using task contracts, failure costs, and a repeatable comparison.
Choosing a model starts with a product decision: what outcome are you paying for? A document assistant, a code reviewer, and a receipt extractor can all use language models, but their definitions of success differ. Comparing them through one general ranking hides the requirements that determine whether the product works.
Consider an illustrative assistant for a bicycle repair shop. It reads a customer message, identifies the bicycle type, and proposes an appointment category. A beautifully written reply is less useful than correctly distinguishing a routine service from a safety concern that needs a technician.
Write the task contract first
Describe the input, the required output, and the cases that must be handed to a person. For the repair shop, inputs might include incomplete messages and photos; outputs might include a service category, missing information, and a draft response. The assistant must not promise stock availability without checking the inventory system.
Give each requirement an observable test. “Understands customers” is difficult to judge consistently. “Requests clarification when the bicycle type is missing” can be checked. Keep several examples where the right behavior is to ask a question instead of finishing the task.
Use model cards to eliminate mismatches
Model cards can document intended uses, limitations, evaluation information, and licensing metadata. Read the card and the actual serving documentation together. A downloadable model and an API offering with a similar name may have different formats, limits, or operational behavior.
Record the exact identifier and deployment route in your shortlist. Reject candidates that cannot accept your required inputs or operate inside your data-handling requirements. This is cheaper than benchmarking an unsuitable option and discovering the mismatch during integration.
Compare the complete workflow
Run the same representative cases through each candidate with the same access to tools and evidence. Include normal requests, ambiguous requests, and malformed inputs. Save the answer, tool results, timing, and validation outcome. A model that succeeds only after several retries may be expensive even when its per-token rate is low.
For the repair shop, score category selection separately from response quality. Otherwise, friendly writing can disguise incorrect routing. Review errors by consequence: asking one extra question and booking an unsuitable repair slot should not receive identical penalties.
Make a decision you can revisit
Set minimum quality requirements before comparing cost. Among candidates that pass, compare cost per completed task, operational complexity, and the waiting time users experience. Keep the evaluation cases and model settings so a later model update can be assessed against the same standard.
Start with a limited rollout and a visible correction path. Capture technician edits as candidate evaluation examples, removing unnecessary personal information. Investigate repeated mistakes before expanding traffic. A shortlist becomes valuable when it connects model behavior to a real decision, with evidence that another person can inspect.
Your first experiment
Take twenty varied, anonymized customer requests and write the expected category and clarification behavior before generating answers. Treat this as an initial diagnostic set, not statistical proof of reliability. Compare two candidates, examine every disagreement, then expand the set around the errors that matter most to the business.