Model selection and evaluation-baseline primer
Compare quality, cost, latency, and deployment constraints on real business questions instead of choosing from leaderboards or demos.
Our position
A leaderboard tells you how smart a model is. It never tells you how badly it fails on your task.
What to take away
- Use real tasks and difficult boundary examples in the evaluation set.
- Put quality, latency, cost, and deployment constraints on one scorecard.
- Consider fine-tuning only when a general model cannot meet the business threshold.
Method breakdown
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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.
- Segment evaluation by real task
- Write reference answers or scoring rules
- Include long, ambiguous, and adversarial inputs
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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.
- Version the prompt and test set
- Record cost and P95 latency
- Score compliance and deployment separately
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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.
- Save versions and complete results
- Add production failures to regression
- Set a minimum pre-release threshold
Action checklist
- Does the evaluation set come from real work?
- Does it include boundary, ambiguity, and hallucination traps?
- Are candidates run under equivalent conditions?
- Does scoring cover quality, cost, latency, and deployment?
- Is there a versioned regression set for future changes?
Who this is for
Product, technical, and business leaders choosing among general or domain models.