Fine-tuning is the process of further training a pre-trained AI model, such as a large language model, on a specific, smaller dataset to adapt its behaviour for a particular task, tone or domain. Rather than building an AI model from scratch, fine-tuning takes an existing general-purpose model and adjusts it using targeted examples, producing a model better suited to a specific business need than the general-purpose version alone.
How fine-tuning works
Fine-tuning involves providing the model with a curated set of example input and output pairs that demonstrate the desired behaviour, such as customer support queries paired with responses written in a business's specific tone of voice. The model's internal parameters are then adjusted based on these examples, shifting its responses to better match the desired style or task. This differs from prompt engineering, which crafts better instructions for an unchanged model rather than altering the model itself, and from retrieval-augmented generation, which grounds responses in retrieved documents rather than retraining the model.
Fine-tuning in practice
- A customer service team fine-tunes a model on examples of their brand's specific tone of voice, producing AI-drafted responses that consistently match company style without needing detailed prompts each time.
- A legal technology business fine-tunes a model on examples of contract clause classification, improving accuracy for this specific, repetitive task beyond what a general-purpose model achieves out of the box.
- A development team considers fine-tuning versus RAG for a custom application and chooses RAG instead, since their need is primarily about answering questions from changing documents rather than adopting a fixed style or skill.
- A business fine-tunes a model specifically for classifying support tickets into categories, achieving higher accuracy than relying on prompt instructions alone for this narrow, repetitive task.
How Advantage approaches fine-tuning
Fine-tuning is rarely the first option for most UK SME AI use cases. Advantage typically recommends prompt engineering or retrieval-augmented generation first, reserving fine-tuning for genuinely specialised, high-volume use cases where it is the right tool for the job.
Frequently Asked Questions
Is fine-tuning the same as prompt engineering?
No. Prompt engineering involves crafting better instructions for an existing, unchanged AI model. Fine-tuning actually retrains part of the model itself on specific example data, permanently adjusting how it responds. Fine-tuning requires more technical effort and a dataset of examples, while prompt engineering requires neither.
Do most businesses need to fine-tune an AI model?
No. For most business use cases, including those addressed by Microsoft Copilot, well-crafted prompts or retrieval-augmented generation using a business's own documents achieve good results without the cost and complexity of fine-tuning. Fine-tuning is more relevant for specialised, high-volume use cases with a consistent, specific style or task.
What kind of data is needed to fine-tune a model?
Fine-tuning typically requires a set of example input and output pairs that demonstrate the desired behaviour, such as example customer queries paired with ideal responses in a business's specific tone and style. The quality and consistency of these examples significantly affects the result.
