blog/llms & ai models
How to Choose an LLM for Your AI Project: Quality, Cost, Speed and Privacy
SoftiIntel Team · · 7 min read
the short answer
Choose an LLM by testing, not reputation. Build a small evaluation set of your real tasks with examples of good answers, run two to four candidate models on it, and compare quality first, then cost per task, speed and data-handling terms. Pick the cheapest model that consistently meets your quality bar. Re-test when new models launch.
1. Define the task precisely
"Answer customer questions" is too vague. "Answer delivery and returns questions from our policy documents, in under 80 words, escalating anything about damaged goods" is testable.
2. Build a small evaluation set
- Collect 20 to 50 real inputs, including hard and unusual ones.
- Write what a good answer must contain (or must avoid).
- Include cases where the right answer is "I don't know, passing you to a person".
3. Compare candidates fairly
- Use the same prompt and data for each model.
- Score quality first. A cheap wrong answer is expensive.
- Then compare cost per completed task and response speed.
4. Check the non-negotiables
- Data handling: training opt-out, retention, where data is processed.
- Availability in your cloud or region if that matters.
- Rate limits and reliability for your volume.
5. Design for change
Models improve every few months. Keep prompts, evaluations and a provider-agnostic layer in your code so switching is a configuration change, not a rebuild. Consider routing easy requests to a cheaper model and hard ones to a stronger one.
Get it done
We run model evaluations on your real data and build the winner into your workflow. See Claude vs ChatGPT vs Gemini and LLM costs explained, or talk to us.