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Start with prompt engineering and a representative evaluation set. Fine-tune only when testing shows a specific, recurring behavior gap that better instructions and examples do not fix—and when you have suitable training data and access to a provider’s fine-tuning service. Fine-tuning is not automatically better; the right choice depends on the task and measured results.
What is the difference between prompt engineering and fine-tuning?
Prompt engineering changes what you send to a model at inference time: the instructions, context, and sometimes examples. OpenAI defines it as writing effective instructions so a model consistently generates content that meets requirements. Because outputs are nondeterministic, prompt changes should be evaluated rather than judged from a single response. See OpenAI’s prompt engineering guide.
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Fine-tuning changes model behavior by training it on examples. In supervised fine-tuning (SFT), those examples pair inputs with desired outputs. OpenAI describes potential use cases such as classification, nuanced translation, specific output formats, and improving instruction-following failures. These examples describe SFT, not every method called fine-tuning by every provider; see OpenAI’s supervised fine-tuning guide.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors| Decision | Prompt engineering | Fine-tuning |
|---|---|---|
| What changes | Instructions, context, and optional examples in each request. | Model behavior, adapted using training examples. |
| Best starting point | The behavior can be described or demonstrated in the request. | A specific, repeated behavior gap remains despite prompt iteration, and representative desired examples are available. |
| Evidence to use | Representative tests comparing prompt versions. | Evaluations set up before training, plus held-out examples to compare the tuned model with its base model. |
| Work involved | Revise request content and test results; inference cost depends on prompt length and the model or provider. | Prepare and curate data, run a training job, and evaluate the result. |
| Operational considerations | Behavior can vary across model snapshots; pin versions and rerun evaluations when changing models. | Provider access, eligible models, data preparation, job management, and model lifecycle. |
There is no general cost or performance winner established by these distinctions. Compare quality, consistency, latency, cost, and maintenance burden for your actual workload rather than assuming either approach is cheaper or faster.
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Should you use prompt engineering or fine-tuning?
Use prompt engineering first when the task can be explained or shown
If the model is missing a requirement because instructions are vague, context is absent, or the desired format is unclear, revise the prompt and measure the change. For a new task pattern, few-shot prompting can include a handful of input/output examples directly in the prompt. OpenAI recommends examples that demonstrate diverse possible inputs; this steers the model without training it. Details are in the prompt engineering guide.
Consider fine-tuning when a stable gap persists
Fine-tuning becomes a candidate when repeated tests reveal a narrow, recurring behavior the prompt does not reliably achieve, and you can provide representative examples of the outputs you want. It is not a substitute for defining success: without evaluations, a training run may change behavior without showing whether it improved the application.
Decide using your deployment, not a general claim
Compare both approaches against the same representative cases. Track task quality and consistency, then measure latency, cost, and ongoing maintenance in the environment where the model will run. These are application-specific decision axes; the available provider guidance does not establish a universal comparative result for them.
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- Define success. Specify what a good output must do, including required content, format, and unacceptable errors.
- Create representative test cases. Include the range of inputs and edge cases the application is expected to handle. Keep cases suitable for comparing revisions.
- Improve the prompt. Clarify instructions and context. Add a diverse handful of input/output examples if they help demonstrate the task pattern.
- Evaluate each change. Compare results against the same cases; do not rely on one convincing response. OpenAI’s guidance on evaluation criteria and graders is in Working with evals.
- Investigate fine-tuning only if a specific gap remains. Confirm that the gap is repeated, the examples are representative, and the provider supports a suitable model and workflow.
- Compare the trained model with the base model. Establish evaluations before training and reserve a representative held-out set so the comparison is not limited to examples used for training. Then assess the deployment trade-offs that matter to your application.
How many examples do you need to fine-tune?
For OpenAI supervised fine-tuning, the current guide documents 10 examples as the minimum, says it sees improvements with 50–100 examples in some cases, and recommends starting with 50 well-crafted demonstrations. These are OpenAI’s guidance figures, not a guarantee, benchmark, or universal threshold; the amount needed varies substantially by use case. Example quality and evaluation matter, so do not treat a sample count as proof that fine-tuning will help. See OpenAI’s supervised fine-tuning documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.OpenAI fine-tuning availability is a current constraint
OpenAI currently says its fine-tuning platform is winding down and is no longer accessible to new users; existing users can create jobs for the coming months. This operational status can change, so check the current fine-tuning guide before planning implementation. This notice concerns OpenAI’s platform and should not be generalized to other providers.
Model behavior can also vary between snapshots. OpenAI recommends pinning model versions and using evaluations to maintain consistent application behavior; see its API Overview. Rerun evaluations when changing models or relevant parameters, and use the same criteria to compare runs; the Evals API reference documents the evaluation interface.
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