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Noroff Case Studies

OpenAI Fine-tuning Case Study

Author: Robert Nilsen <Padletut>

Introduction

This case study digs into OpenAI’s fine-tuning API, a feature I evaluated for the Development Platforms assignment to understand how support-focused assistants can stay on-brand. Fine-tuning takes a pre-trained model—already steeped in huge public datasets—and teaches it on a smaller, curated set. The result is a model that remembers formatting rules, matches tone guidelines, and feels less like a generic chatbot.

While preparing this report I explored the most common “shape the model” strategies. Prompt engineering works until the model forgets half the instructions. Retrieval-Augmented Generation (RAG) is brilliant for fresh facts, but it adds infrastructure overhead. Fine-tuning stood out because it delivered consistent tone and structure without lengthening prompts.

OpenAI keeps the experience approachable: the dashboard walks through setup, the API is familiar if you’ve used their other endpoints, and the docs answer most questions before you hit a wall. That combination of cutting-edge tooling and reasonable developer ergonomics is why I selected it for this write-up.

Brief History

This timeline focuses on the development of OpenAI’s GPT models and the introduction of fine-tuning as a product feature. While the broader history of natural language processing dates back decades, this section highlights milestones that directly shaped the fine-tuning capabilities offered through the OpenAI API.

  • 2015: OpenAI is founded as an artificial intelligence research company with the mission to ensure that artificial general intelligence benefits all of humanity.
  • June 2018: OpenAI introduces GPT (Generative Pre-trained Transformer), the first large-scale language model trained with a transformer architecture.
  • February 2019: GPT-2 is released, showcasing the potential of large-scale language models and sparking debates about safety and misuse.
  • June 2020: GPT-3 is launched with 175 billion parameters, dramatically improving fluency and contextual understanding.
  • 2021: OpenAI introduces fine-tuning support for GPT-3, allowing developers to adapt the model to specific use cases and styles.
  • November 2022: ChatGPT is released to the public, based on GPT-3.5, and rapidly becomes one of the fastest-growing consumer applications.
  • March 2023: GPT-4 is introduced with improved reasoning and safety features; developers gain more control through the ChatGPT API.
  • May 2024: OpenAI expands fine-tuning to the GPT-4.1 family, introducing Direct Preference Optimization (DPO) to improve alignment with user intent.
  • 2025: Fine-tuning continues as a key offering in the OpenAI API, providing businesses and developers with a way to create domain-specific AI systems that go beyond what prompt engineering or retrieval alone can achieve.

Main Features

OpenAI’s fine-tuning API gives developers a straightforward way to adapt large pre-trained models to specific use cases. In the test runs I set up for this assignment, it was the first time a model actually stuck to a support tone guide without me pasting three paragraphs of instructions. Fine-tuning solves problems of tone, formatting, and domain specificity—areas where prompt engineering alone kept letting me down.

FeatureDescription
Custom Output StyleFine-tuned models can consistently respond in a specific tone, format, or personality. For example, customer support bots can always reply in a professional and polite style, without needing long prompts.
Domain-Specific KnowledgeDevelopers can train the model on specialized datasets (e.g., medical, legal, or technical language) so it produces accurate and relevant outputs in niche fields.
Reduced Prompt ComplexityOnce fine-tuned, the model “remembers” instructions, allowing shorter prompts and faster responses, which also lowers inference costs at scale.
Integration with APIFine-tuned models are deployed as unique model IDs within the OpenAI API, meaning they can be used immediately in existing applications with minimal changes to code.
ScalabilityBy aligning a model to repetitive, high-volume tasks (such as ticket classification or FAQ responses), organizations can achieve both cost efficiency and consistent customer experience.
Advanced Optimization MethodsOpenAI supports modern methods such as Direct Preference Optimization (DPO) and reinforcement fine-tuning, enabling alignment with user preferences and safer outputs.

Why and When to Fine-tune

Large language models can be shaped in several ways, and I compared the main approaches while researching this case study. Two standbys—prompt engineering and retrieval-augmented generation (RAG)—are useful, but each has gaps that pushed me toward fine-tuning for day-to-day reliability.

Prompt Engineering

  • What it is: Writing detailed instructions in the prompt to guide the model.
  • When it helps: Rapid experiments or one-off tasks. It’s free and flexible, but the model still wanders off-script when the conversation gets long.

Retrieval-Augmented Generation (RAG)

  • What it is: Supplying the model with external documents or data during inference.
  • When it helps: Knowledge-heavy queries where freshness matters. Accuracy stays high, but you’re signing up for extra infrastructure and monitoring.

Fine-tuning

  • What it is: Training the model further on domain-specific examples so it learns a consistent style, tone, or format.
  • When it helps: Customer support, domain assistants, or structured outputs. The upfront data prep is real, but the reward is replies that finally sound like your team.

Example: Noroff Support Bot

This is how the three approaches could play out for a hypothetical Noroff support bot.

  • Prompt engineering: “You are Noroff support. Reply briefly, politely, and professionally.” → Works, but responses vary.
  • RAG: “What is the submission deadline for the FEU course?” → The bot retrieves the deadline from documents and answers correctly.
  • Fine-tuning: After training on hundreds of support conversations, the bot always replies in the required format.

“Hello! Thank you for reaching out. The deadline for FEU submissions is October 19. Please contact us again if you have more questions.”

Market Comparison

OpenAI’s fine-tuning API is one of several ways to shape a model, but from a student developer perspective it feels like the option that would let you ship something without begging for enterprise credentials. Compared to alternatives, it stands out for simplicity and low friction.

A close competitor is Microsoft’s Azure OpenAI Service, which exposes the same GPT models and fine-tuning capabilities. The difference I noticed while testing both is mostly context: Azure wraps everything in enterprise-grade governance. Great if your company already lives in Azure; overkill when you’re hacking on a campus project and just want an API key.

At the other extreme are open-source solutions like Hugging Face models fine-tuned with LoRA or QLoRA. Total control, lower inference costs, and a chance to touch the underlying weights. I love the idea, but sourcing GPUs and running the ops pipeline felt like a second capstone project.

For now, OpenAI’s managed workflow hits the sweet spot for my use case: enough knobs to get a professional result, without the operational overhead that would swallow a small team’s time.

Getting Started

Getting started with OpenAI fine-tuning is straightforward, especially through the dashboard or API. I treat the process as a three-step checklist: prepare data, launch the job, wire up the new model ID.

1. Prepare Your Dataset

  • Format data as JSONL (JSON Lines), where each line contains a training example.
  • For chat models, each example includes a messages array with user/assistant roles.
  • I recommend keeping a tiny validator script in Node to catch typos before uploading—the API is strict about commas and quotes.

Example:

{"messages":[{"role":"user","content":"I cannot log into the platform."},{"role":"assistant","content":"Hello! Thank you for contacting us. Have you tried resetting your password using the 'Forgot password' link?"}]}
{"messages":[{"role":"user","content":"How do I change my email address?"},{"role":"assistant","content":"Hello! You can update your email by going to your account settings and selecting 'Update email'. Please reach out again if you need more help."}]}
{"messages":[{"role":"user","content":"When is the deadline for the FEU course assignment?"},{"role":"assistant","content":"Hello! The deadline for the FEU course assignment is October 19 at 23:59. Please let us know if you have more questions."}]}
{"messages":[{"role":"user","content":"Can I submit my assignment late?"},{"role":"assistant","content":"Hello! Late submissions are generally not accepted, but please contact your instructor directly to discuss your situation."}]}
{"messages":[{"role":"user","content":"Where can I download course materials?"},{"role":"assistant","content":"Hello! Course materials can be downloaded directly from the learning platform under the 'Resources' section of your course page."}]}
{"messages":[{"role":"user","content":"How do I reset my two-factor authentication?"},{"role":"assistant","content":"Hello! To reset your two-factor authentication, please visit the security settings in your account. If you are locked out, contact IT support for assistance."}]}
{"messages":[{"role":"user","content":"Do I need to buy textbooks?"},{"role":"assistant","content":"Hello! Most required reading materials are provided in digital format on the platform. If additional textbooks are needed, your instructor will let you know."}]}
{"messages":[{"role":"user","content":"Can I get an extension on my project?"},{"role":"assistant","content":"Hello! Extensions are only granted under special circumstances. Please submit a request to your course instructor with an explanation."}]}
{"messages":[{"role":"user","content":"How do I contact my instructor?"},{"role":"assistant","content":"Hello! You can contact your instructor via the messaging function in the learning platform or through their official email."}]}
{"messages":[{"role":"user","content":"I am having trouble uploading my assignment."},{"role":"assistant","content":"Hello! Please make sure your file is under the size limit and in the correct format. If the issue persists, try another browser or contact IT support."}]}

2. Create a Fine-tuning Job

  • In the OpenAI dashboard, go to Optimize → Fine-tuning and click Create.
  • Alternatively, use the API. For example, with the official JavaScript client:
import OpenAI from "openai";


const client = new OpenAI();


async function main() {
    try {
        const job = await client.fineTuning.jobs.create({
            training_file: "file-abc123",
            model: "gpt-4.1-mini"
        });


        console.log(job.id);
    } catch (error) {
        console.error(error);
    }
}


main();
  • The job runs asynchronously. You can monitor its progress in the dashboard or via the list_events endpoint.
  • Don’t forget to set your OPENAI_API_KEY locally; storing it in a .env file keeps scripts portable.

3. Deploy and Use the Model

  • When training completes, the API provides a new model ID.
  • Replace the base model with this fine-tuned model in your application:
import OpenAI from "openai";


const client = new OpenAI();


async function main() {
    try {
        const response = await client.chat.completions.create({
            model: "ft:gpt-4.1-nano-2025-04-14:personal:noroff-example:abc123",
            messages: [{ role: "user", content: "I cannot log into the platform." }]
        });


        console.log(response.choices[0].message.content);
    } catch (error) {
        console.error(error);
    }
}


main();
  • The model now consistently responds in the style and format you trained it on.
  • Logging the model ID and training run URL somewhere central (Notion, classroom docs, etc.) helps teammates plug it into demos without hunting through the dashboard.

Resources

Conclusion

OpenAI’s fine-tuning API emerged from this research as the most dependable way to get a language model behaving like a real teammate instead of a generic bot. The big wins are consistent tone, shorter prompts, and integration that feels like the rest of the OpenAI stack. The trade-offs are classic product realities: you need clean training data, and you pay for the extra training runs.

If you’re building customer support bots, domain-specific assistants, or anything that spits out structured answers, fine-tuning earns its keep fast. Just be aware you’re committing to OpenAI’s platform and aligning your release cycles with theirs. For coursework prototypes like this, that trade feels worthwhile—the time saved on prompt wrangling more than covers the setup effort—and it’s easy to see why more teams chase fine-tuned, brand-aligned experiences.

References

Additional Resources