Start with prompt engineering and evaluation. Improve the instructions, context, and examples you send at inference time. Fine-tune only when testing shows a persistent, specific behavior gap and you have suitable training examples and access to a supported fine-tuning service. Fine-tuning is not automatically a better or cheaper version of prompting.
What is the difference between prompt engineering and fine-tuning?
Prompt engineering changes what you ask the model to do in each request: its instructions, context, and, when useful, examples. OpenAI defines it as writing effective instructions so a model consistently generates content that meets requirements. Because model outputs are nondeterministic, a prompt that seems to work on a few cases still needs evaluation against representative examples. OpenAI’s prompt engineering guide explains the approach and its limitations.
Fine-tuning changes model behavior by training it on examples. In supervised fine-tuning (SFT), those examples pair inputs with known-good outputs for a particular use case. The details below about training methods and example counts refer specifically to OpenAI’s SFT guidance; other providers may offer different methods, eligibility, and requirements. OpenAI’s SFT guide describes its method and suggested uses.
| Decision point | Prompt engineering | Fine-tuning |
|---|---|---|
| What changes | Instructions, context, and optional examples in the request | Model behavior, adapted using training examples |
| Best starting use | The desired behavior can be clarified or demonstrated in the prompt | A repeated, specific behavior remains inadequate and representative desired outputs are available |
| What to evaluate | Whether prompt revisions improve results on representative test cases | Whether the tuned model improves on a held-out evaluation set compared with its base model |
| Typical work involved | Revise request content and test results | Prepare a curated dataset, run training, and evaluate the result |
| Key operational concern | Behavior can vary between model snapshots; pin versions and rerun evaluations when models change | Supported models, provider access, data preparation, job management, and model lifecycle |
Should you use prompting or fine-tune?
Choose based on evidence from your application, not on an assumption that training is inherently more powerful. Define what a successful output looks like, test the current approach, and see whether a prompt change can close the gap. Consider fine-tuning when the same specific failure persists and you can supply representative examples of the behavior you want.
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- Use prompt engineering first when the task can be explained more clearly, additional context can resolve ambiguity, or examples can demonstrate the desired pattern.
- Try few-shot prompting when a new task pattern is hard to express in instructions alone. Include a handful of examples that show diverse possible inputs and the corresponding outputs; this steers behavior through the prompt rather than training the model. OpenAI’s guide to prompting describes few-shot learning.
- Investigate fine-tuning when evaluation shows a stable, repeated behavior gap despite prompt improvements, and you have suitable input/output examples plus access to a provider and model that support the method.
OpenAI lists classification, nuanced translation, specific output formats, and correcting instruction-following failures among SFT use cases. These are examples of tasks to consider, not a guarantee that fine-tuning will improve every application. The SFT documentation describes these use cases.
How to decide, step by step
- Define success. Write down the output requirements and how you will judge them, including what counts as a failure.
- Build representative test cases. Include the kinds of inputs your application will actually encounter, not only easy or ideal examples.
- Improve the prompt. Clarify instructions and relevant context. Add a diverse handful of input/output examples if they help show the expected pattern.
- Evaluate the revision. Compare results against the original prompt on the same test cases and criteria. OpenAI’s evals guide covers evaluation criteria and graders; its Evals API reference includes tools for working with evaluation runs.
- Fine-tune only for a demonstrated gap. If the same important failure remains, confirm that your data is representative and that a suitable fine-tuning service and model are available.
- Compare the trained model with its base model. Evaluate both on similar, held-out cases. Also measure latency, inference cost, and maintenance effort for your own workload; there is no universal cost or performance winner.
OpenAI’s guidance is direct: “Good evals first! Only invest in fine-tuning after setting up evals.” Its SFT guide recommends establishing evaluations before training and reserving a representative holdout set for comparison.
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How many examples do you need to fine-tune?
For OpenAI supervised fine-tuning, the documentation accessed in 2026 says the minimum is 10 examples, notes that it sees improvements in some cases with 50–100, and recommends starting with 50 well-crafted demonstrations. These are OpenAI’s guidance figures—not a benchmark, a promise of improvement, or a universal threshold. The appropriate amount varies substantially by use case. Check the current SFT guide for requirements before preparing a dataset.
Example quality and relevance matter to the task: use examples that represent the inputs you expect and show the outputs you want. Keep some representative cases out of the training data so you can test whether the tuned model generalizes rather than merely reproducing training examples. Compare those results with the base model using the same evaluation criteria.
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What can change the decision in production?
Model versions and consistency
Prompt behavior can change between model snapshots. Pin a model version where possible and rerun evaluations when changing models or other important parameters. OpenAI’s prompt engineering guide and API overview discuss model snapshots and maintaining consistent application behavior.
Provider access and lifecycle
As of OpenAI’s documentation accessed in 2026, its fine-tuning platform is winding down and is unavailable to new users; existing users can create jobs for the coming months. This status can change, so check the current SFT guide before planning implementation. This OpenAI-specific notice does not establish the availability or lifecycle of fine-tuning from other providers.
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Cost, latency, and maintenance
Prompting is often the simpler iteration path because it changes request content rather than requiring a training job. But prompt length affects the request, and actual inference costs depend on the model and provider. Fine-tuning adds work to curate data, train, evaluate, and manage a tuned model. Neither approach is categorically cheaper or faster: compare quality, consistency, latency, cost, and maintenance for the particular deployment.
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