Model selection and evaluation-baseline primer
Our positionA leaderboard tells you how smart a model is. It never tells you how badly it fails on your task.
Compare quality, cost, latency, and deployment constraints on real business questions instead of choosing from leaderboards or demos.
You are probably stuck here
- 01The top-ranked model underperforms on our own tasks
- 02We swapped models and quality drifted without anyone noticing
- 03Cost and latency only became visible after launch
If one of these is yours, the rest of this page is worth your time.
After reading, you should be able to answer
- 1How to build an eval set from 100 real questions
- 2How to read accuracy, cost and latency together
- 3How to keep quality from drifting across model swaps
Who this is for
Product, technical, and business leaders choosing among general or domain models.
What you'll take away
- 01Use real tasks and difficult boundary examples in the evaluation set.
- 02Put quality, latency, cost, and deployment constraints on one scorecard.
- 03Consider fine-tuning only when a general model cannot meet the business threshold.
Method breakdown
- 01
Define the task before the model
Summarization, classification, extraction, and open-ended answers have different demands. Gather representative inputs, define expected outputs, and state tolerable errors so the question becomes which model fits the task—not which model is strongest overall.
What this step asks for01- Segment evaluation by real task
- Write reference answers or scoring rules
- Include long, ambiguous, and adversarial inputs
- 02
Compare candidates under the same conditions
Run the same prompt, data, and settings across candidates. Record response time, unit cost, concurrency, retention policy, and private-deployment support alongside quality so the winner can actually operate in production.
What this step asks for02- Version the prompt and test set
- Record cost and P95 latency
- Score compliance and deployment separately
- 03
Make evaluation continuous
Models, prompts, and knowledge change. Preserve failures as a regression set and rerun it after every material change to confirm that an improvement in one area did not break an existing capability.
What this step asks for03- Save versions and complete results
- Add production failures to regression
- Set a minimum pre-release threshold
- Back to step 01 — run it again
Three steps are a cycle, not a checklist: after one pass, what step 01 should measure has changed.
Action checklist
Tick what you already have. Whatever is left unticked is your next move.
5 still open. Do not fix them all at once; start with the one that hurts.
Take this page with you
Model selection and evaluation-baseline primer
- Our position
- A leaderboard tells you how smart a model is. It never tells you how badly it fails on your task.
- You are probably stuck here
- The top-ranked model underperforms on our own tasks; We swapped models and quality drifted without anyone noticing; Cost and latency only became visible after launch
- After reading, you should be able to answer
- How to build an eval set from 100 real questions; How to read accuracy, cost and latency together; How to keep quality from drifting across model swaps
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