These days, every business wants «its own» artificial intelligence: a tool that understands their industry, speaks their language and automates their repetitive tasks. But there are several ways to tailor AI to your needs, and choosing the wrong one can cost you time, money and a great deal of frustration.
Should you settle for a generic model like ChatGPT? Connect it to your documents using RAG? Or train your own model through fine-tuning? These three approaches do not meet the same needs and do not involve the same budget or the same level of confidentiality. In this article, we compare them in detail, looking at their advantages, limitations and the situations in which each is most appropriate.
The three main ways to adapt AI to your business
1. The generic «ready-to-use» template»
This is the simplest approach. You use an existing language model (OpenAI, Anthropic, Google, etc.) as it is, guiding it solely through written instructions – known as a prompt. No training data, no infrastructure to set up: you get results in a matter of minutes.
The main advantage is the speed and low initial cost. It’s ideal for testing an idea, providing occasional assistance or automating very general tasks such as writing, summarising or brainstorming.
The limitations quickly become apparent as soon as the requirement becomes specific. The model knows nothing about your products, your procedures or your history. Its responses remain generic; it sometimes makes up information with confidence; and, above all, your data passes through an external service. When it comes to confidential information, this is a real cause for concern.
2. RAG: connecting AI to your knowledge
RAG (which stands for Retrieval-Augmented Generation) connects a model to your document repository. Whenever a question is asked, the system first searches for relevant documents within your content (procedures, product sheets, contracts, records), then passes them to the model so that it can formulate a response based on your information.
There are two benefits to this. Firstly, the responses are based on your up-to-date information rather than on the model’s general knowledge. On the other hand, updates are immediate: when a document changes, you simply update it in the database without needing to retrain the model. This is the ideal approach for a knowledge base that changes frequently, such as an internal help centre, FAQs or technical documentation.
Its limitation lies in its very nature. The RAG feeds information into the model, but it does not alter its behaviour. It does not teach it a particular style, a specific output format, or a recurring classification task. To achieve this, one must move on to the next stage.
3. Fine-tuning: training your own model
Fine-tuning involves specialising a pre-trained model by retraining it on your own examples. The model then learns your decisions, your vocabulary, your tone and your exceptions. You end up with a bespoke AI, which you can often host on your own servers and which you truly own.
There is considerable benefit for specific, recurring tasks. Accuracy improves significantly, performance becomes consistent (same format, same tone and same categories from one instance to the next) and you can work with sensitive data in-house. This is the go-to approach for document classification, information extraction, fraud detection or writing in your brand’s voice.
On the other hand, it requires a high-quality dataset and a little more preparation beforehand. But this initial effort builds a real asset, which increases in value as you enrich it. This is precisely what we support with our [bespoke AI fine-tuning service].
The comparison at a glance
Summarised in a few sentences, these three approaches differ mainly in terms of what they change and what they require of you. The generic model does not alter the model itself: you run it from the command line, without any data or infrastructure, making it the quickest and cheapest option to get started with.
It is also the least accurate when it comes to your specific cases and offers the least protection for your information, as it is routed through an external service. Nor does RAG affect the model’s behaviour; rather, it feeds it with your documents. It therefore relies on your document repository being available and well-maintained, updates instantly as your content changes, and keeps your information under your control. This strikes the ideal balance when knowledge changes frequently.
Fine-tuning, on the other hand, is the only one of the three that actually modifies the model. It requires a set of labelled examples and thorough preparatory work beforehand, and it involves periodic retraining to keep the model up to date.
In return, it offers the highest level of accuracy for specialised tasks, the highest level of confidentiality (on-premises hosting remains an option) and, above all, a model that you own. In practice, therefore, you should use the generic model for testing or general purposes, RAG for querying evolving knowledge, and fine-tuning for a specific task, consistent behaviour or sensitive data.
When is fine-tuning the right choice?
Fine-tuning is essential in a number of typical situations. It is particularly useful when you have a repetitive and precise task to automate, such as sorting incoming documents, extracting structured data or detecting anomalies. It is equally relevant when you require consistent behaviour, such as a reliable output format, a consistent brand tone or the use of industry-specific jargon. It becomes almost essential as soon as your data is confidential and you do not wish to expose it to third-party services. Finally, it is the only option if you want to own a proprietary AI system, which you can host and develop freely.
Conversely, there’s no need to bring out the big guns if all you need to do is search through documentation that changes every week. RAG will be more cost-effective and better suited to the task. And for a simple test or one-off use, a generic model will do the job perfectly.
Common mistakes to avoid
Many AI projects fall short not because of the technology itself, but because they are poorly defined. Three pitfalls crop up time and again.
The first involves adopting a trend-driven approach rather than one based on use cases. Fine-tuning a model to answer questions about ever-changing documentation, for example, is simply making life more difficult for yourself when a RAG would suffice.
The second pitfall is to overlook the quality of the data. A model fine-tuned on incomplete, mislabelled or unrepresentative examples will faithfully reproduce these flaws. Data preparation is often more important than the choice of model itself.
The third is to forget about what comes next. AI is not a project that you deliver and then tuck away in a drawer. Without performance monitoring or retraining, a model deteriorates as the reality around it changes.
What if the best answer combined several approaches?
In practice, the most effective solution often combines all three. For example, you can fine-tune a model so that it adopts the correct format and tone, then incorporate RAG to ensure it draws on your most up-to-date information. The whole process is then integrated into your tools via a AI application or AI agents connected to your ERP or CRM.
Let’s take a practical example. A company wishing to automate its customer support responses can fine-tune a model so that it adopts the brand’s tone and adheres to internal procedures, whilst connecting it via RAG to the product knowledge base, which evolves with every update. The customer then receives a response that is both factually accurate and consistent in style.
The key, therefore, is not to choose the most high-profile technology, but to start with your specific use case and your constraints in terms of cost, data privacy and data volume, in order to build the most cost-effective architecture, without being dependent on a single supplier.
How much time and money should you set aside?
There is no single fixed price, as it all depends on the complexity of the task and the condition of your data. An initial pilot project, carried out as a prototype, enables us to gauge the impact before investing further. This step-by-step approach – starting small, assessing the results and then scaling up – minimises risks and avoids never-ending projects that never come to fruition. During an initial discussion, we assess the feasibility, the volume of data required and the approximate budget, so that you can move forward with full knowledge of the facts.
From theory to your project
Are you unsure which approach is right for your business? That is precisely where every successful project begins. At iterates, we analyse your data, your use case and your objectives, and then design the most appropriate solution, from a simple prototype to a fully bespoke AI model.
Discover our [AI fine-tuning service in Brussels] or Arrange a consultation with our experts to help you define the scope of your project.
To find out more, have a read of our article: Detect fraudulent invoices using AI trained on your data
