Inference Parameters and Model Selection
Select a language model and generation settings by measuring task quality, format reliability, latency, and cost.
Quick answer
Choose models and inference settings by testing representative tasks against correctness, format reliability, latency, cost, and deployment requirements. Sampling parameters influence output selection but do not guarantee a correct answer, and parameter behavior varies by provider and model.
- Record the exact model identifier and test date; names, limits, and features change.
- Change one parameter at a time and evaluate with fixed examples and prompts.
- Validate structured outputs and tool actions in the application even when model features constrain format.
Hold fixed: evaluation inputs, prompt version, model version
Change: one supported parameter
Measure: task correctness, valid format, latency, token usage
Keep: the setting only if the task-specific trade-off improvesLearning objective
Select a model and generation settings using repeatable task tests—not intuition alone.
Prerequisites: understand next-token generation, prompts, and context budgets. Review Prompt Engineering and Context Windows and Model Limits first.
Choose for the task you need to solve
Start with a representative evaluation set: real examples, expected outcomes, and known edge cases. Compare candidate models against the same prompts and inputs. A model that is strongest on a general benchmark may still be a poor fit for your task, output format, latency target, or deployment constraints.
| Dimension | Test it |
|---|---|
| Task quality | Does it answer correctly on your representative examples and fail safely on ambiguous ones? |
| Input and output fit | Does the documented context and output capacity cover your request shape? |
| Format reliability | Does the model return valid JSON or a tool call when your application requires it? |
| Latency and throughput | Does it meet measured response-time and concurrency needs under realistic load? |
| Cost | Measure actual input and output usage, including retries and long conversations. |
| Data handling and availability | Check the provider’s current terms, retention controls, region availability, and operational requirements. |
What generation settings can—and cannot—do
A model assigns scores to possible next tokens. Sampling settings influence how a response is selected from those possibilities; they do not repair missing knowledge, an unclear task, a weak retrieval result, or an unsuitable model.
| Control | Practical interpretation |
|---|---|
| Temperature | Changes sampling randomness in APIs that support it. Lower values often reduce variation; they do not guarantee correctness or identical output. |
| Top-p | Limits sampling to a probability mass chosen by the API. It is an alternative sampling control in some providers; avoid tuning it and temperature together without an evaluation plan. |
| Maximum output tokens | Caps response generation. It is a ceiling, not a promise that the response will use that many tokens. |
| Stop sequences | Can stop generation at configured text in APIs that support them. A poorly chosen sequence can cut off a valid answer. |
| Structured output / tool controls | Constrain the response shape or expose tools when supported. Validate outputs and authorize tool actions in your application regardless. |
Tune with a small controlled experiment
- Write down the task, acceptable answer, and failures that matter.
- Fix the evaluation inputs, prompt version, and model version.
- Change one setting at a time and run the same examples.
- Measure task correctness, valid-format rate, refusal/edge-case behavior, latency, and token usage.
- Keep a setting only when the measured trade-off improves the intended outcome.
For extraction into a schema, compare valid and correct records—not just whether the output looks tidy. For brainstorming, assess useful variety and relevance rather than rewarding randomness. The desired setting depends on what the product considers a good answer.
Practice: choose a setup for two tasks
Task A: classify support messages into one of six fixed categories and return a machine-readable result.
Task B: suggest several different campaign ideas from a short brief.
For each task, choose candidate models that meet the documented input and output needs. Define a test set, hold the prompt stable, and compare settings based on the task’s success criteria. For Task A, include malformed or ambiguous cases and validate the returned structure. For Task B, ask reviewers to rate relevance and useful diversity. Do not assume one temperature value—or one model—will be best for both.
Before shipping, record model identifier, API version, parameters, test cases, results, latency, and cost. Re-test after changing any of them.
Key takeaways
- Select a model against representative task tests and real product constraints.
- Generation controls influence sampling or response limits; they are not quality guarantees.
- Parameter support and semantics vary—verify the current model and API documentation.
- Change one setting at a time and evaluate correctness, reliability, latency, and cost.
Quick check
What is the reliable way to choose a generation setting?
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