To help a model do something new, first identify what is failing: its instructions, its access to information, its recurring behavior, or its ability to take action. Then choose the matching method—prompting, retrieval, fine-tuning, or tools—and compare the result with a baseline on representative test cases. These methods change different parts of a system; they are not interchangeable.
What does it mean to customize a model?
“Customize” can describe several distinct changes to an AI application:
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- Prompting changes the instructions and examples supplied with a request. The model’s trainable parameters stay the same.
- Retrieval-augmented generation (RAG) finds relevant material in an external collection and supplies it to the model at response time.
- Fine-tuning trains a model on examples of inputs and desired outputs to shape how it handles recurring tasks.
- Tool integration lets the application route a request to an external capability, such as searching documents or calculating a value.
A model that lacks current facts needs a different fix from one that repeatedly ignores a required output format. Start with the observed failure rather than assuming every new task calls for training.
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Before changing anything, collect representative inputs and define what a good answer or action looks like. Include ordinary cases and important edge cases; keep the expected result or evaluation criteria alongside each case. Run the current system on this set so you can tell whether a change actually helped.
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This matters because language-model output is nondeterministic, and behavior can change between model snapshots and model families. OpenAI’s model optimization guidance recommends an iterative cycle of evaluation, prompting, optional fine-tuning, and evaluation again. Keep test cases consistent when comparing versions, and inspect failures rather than relying only on an overall score.
Choose the method that addresses the failure
| Observed need | First method to try | What it changes |
|---|---|---|
| The model misunderstands the task or response format | Clearer instructions and examples | The request context, not the model’s trainable parameters |
| The model lacks current, private, or frequently updated facts | Retrieval-augmented generation | The information supplied at response time |
| The model repeatedly misses a behavior despite good instructions and context | Fine-tuning | Task behavior through training examples |
| The model must search, calculate, fetch, or execute something | Tool or function integration | The application’s access to an external capability |
You can combine methods where needed. This is a decision framework, not a mandatory sequence: change the lever that matches the failure, then evaluate the result.
Improve instructions and examples first when the task is unclear
State the task, constraints, relevant context, and desired output explicitly. If a pattern is easier to demonstrate than to describe, add a few examples showing both the input and the response you want. Then rerun the same evaluation cases and check whether the specific failure improves.
Prompting is often the most direct way to address ambiguity or formatting problems because it changes what the model sees for the request without changing its learned parameters. If the model still fails consistently despite clear instructions and useful context, consider whether the issue is instead missing information, a need for external action, or a recurring behavior that training examples could address.
Use retrieval when the answer depends on external knowledge
RAG retrieves relevant material from an external collection and adds it to the model’s prompt. It is useful when information is private, absent from the model’s training, or likely to become outdated. Because the source material remains outside the model’s parameters, you can update the collection without treating each new fact as a training example.
Evaluate two parts of the system separately:
- Retrieval: Does the system find the material needed to answer the question?
- Generation: Given that material, does the model use it correctly in its response?
A wrong answer can result from either failure. Checking only the final response makes it harder to know whether to improve the search process, the supplied context, or the model’s use of that context.
Fine-tune for recurring behavior, not as a shortcut to current facts
Supervised fine-tuning (SFT) trains on examples of inputs paired with desired outputs. It may help when a model repeatedly struggles with a task behavior, consistent output format, or instruction-following even after the instructions and context are in good shape. It is not a substitute for providing facts that change regularly; retrieval is generally the more direct way to supply those facts at response time.
Establish an evaluation baseline before training, and curate examples that represent the task you want the model to perform. OpenAI’s supervised fine-tuning guidance recommends starting with 50 well-crafted demonstrations. It also states a minimum of 10 examples and says the appropriate amount varies by use case. Fifty is a starting recommendation, not a universal threshold or a guarantee of improvement.
After training, compare the tuned checkpoint against the baseline using the same evaluation cases. Look for regressions as well as gains: a model may improve on the examples emphasized in training while performing worse on other representative inputs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Add tools when the application must do something
A tool gives the application a route to an external operation—for example, searching a document store, calling a function, or performing a calculation. The model can decide when to request a tool, but the application has to execute the operation and return its result. This expands what the system can do; it does not mean the model has absorbed the tool’s data or capability into its weights.
Test the complete flow, not just the model’s decision to call a tool: whether it selects the right operation, supplies usable inputs, handles the result correctly, and responds appropriately when the operation fails. Tool frameworks and APIs vary; implementation instructions should match the provider and version you actually use.
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Availability is not uniform across providers. As documented on October 8, 2026, OpenAI said it was winding down its fine-tuning platform and that it was no longer accessible to new users. Existing platform users could create training jobs for the coming months, while fine-tuned models would remain available for inference until their base models were deprecated. This is a dated status for OpenAI’s platform, not a claim about fine-tuning everywhere. Check current model support and account eligibility before building a workflow around a specific service.
Learn the concepts in a structured sequence
For readers who want a book-length introduction, O’Reilly lists Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst as a September 2024, 428-page beginner-to-intermediate book. Its contents include prompt engineering, semantic search, RAG, and training and fine-tuning language models: O’Reilly book listing.
For a more focused, advanced treatment of retrieval, O’Reilly lists Hands-On RAG for Production by Ofer Mendelevitch and Forrest Sheng Bao, dated May 2026, for intermediate-to-advanced readers. It focuses on RAG and discusses how it compares with fine-tuning: O’Reilly book listing.
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