With many capable large language models available, public benchmark leaderboards are a poor substitute for evaluating whether a specific model will work well for your specific workflow. A more useful evaluation starts with the workflow itself.
Start with a defined workflow and test set
Before comparing models, define the exact task the model needs to perform and assemble a representative test set of real (or realistic) examples from that workflow, including edge cases. Evaluating against your own test set tells you far more than a generic public benchmark.
Accuracy and task success
Define what "correct" means for your specific task and measure it directly against your test set—not a proxy metric borrowed from a different use case.
Hallucination and safety risk
For workflows where a confidently wrong answer is costly, test specifically for hallucination rate and for how the model behaves on inputs designed to probe its limits, not just its performance on well-formed examples.
Latency and reliability
Measure response time and failure rate under conditions that resemble your actual production load, not a single best-case test call.
Privacy and data handling
Understand exactly how each candidate provider handles your data—whether it's used for further model training, where it's processed and stored, and what contractual protections are available—before it becomes part of your evaluation shortlist, not after.
Integration effort
Factor in the engineering cost of integrating each candidate: API compatibility, prompt engineering and tooling required, and how much custom infrastructure you'd need to build around it.
Total operating cost
Look beyond the per-token price to the total cost at your expected volume, including retries, longer prompts needed for accuracy, and any human review built into the workflow.
Human evaluation and production monitoring
Automated metrics rarely capture everything that matters. Pair them with structured human review during evaluation, and keep monitoring quality in production after launch—model behavior can drift as providers update models behind the same API.
| Evaluation dimension | Weight | Illustrative score (1–5) |
|---|---|---|
| Accuracy / task success | High | Illustrative — score against your own test set |
| Hallucination & safety risk | High | Illustrative — score against your own test set |
| Latency & reliability | Medium | Illustrative — score against your own test set |
| Privacy & data handling | High | Illustrative — pass/fail against your requirements |
| Integration effort | Medium | Illustrative — score against your own test set |
| Total operating cost | Medium | Illustrative — score against your own test set |
Key takeaways
- Evaluate against a test set built from your actual workflow, not a generic public benchmark.
- Weight hallucination and safety risk seriously for any workflow where a confident wrong answer is costly.
- Understand each provider's data handling before it's on your shortlist, not after you've integrated it.
- Keep monitoring quality in production—model behavior can drift even behind an unchanged API.
Want to talk through how this applies to your organization?
Design an LLM evaluation