RAG, fine-tuning, prompt engineering : these three methods make it possible to adapt a model of’generative artificial intelligence They are all suited to a specific context, but they do not meet the same needs and do not involve the same costs. Understanding the differences between them is the first step towards making the right choice for your situation.
Prompt engineering: the quickest approach to implement
Le prompt engineering involves formulating precise instructions to guide the behaviour of an existing AI model, without modifying the model itself. This is the most accessible method: it requires no special infrastructure or model training budget, and can produce useful results within a few hours.
What prompt engineering actually enables
A well-designed prompt can transform a general-purpose model into an assistant capable of responding in the company’s tone of voice, analysing documents according to specific criteria, or generating coherent content. This is the ideal approach for standard use cases – such as writing, summarising and classification – where the model does not need access to proprietary data.
The limitations of prompt engineering alone
Prompt engineering has its limitations: the model only knows what it is told during the conversation. It cannot access internal data or information that arose after its training. As soon as a company needs a tool that is rooted in its knowledge base, it is necessary to go a step further.
RAG: connecting AI to your internal data
Le RAG (Retrieval-Augmented Generation) combines a language model with a document retrieval system. Rather than entering everything into the prompt, the system retrieves relevant information in real time from an internal document database and passes it to the model to generate a response grounded in that data.
Why RAG is the right solution for most SMEs
For an SME looking for an AI assistant that can provide answers based on its internal documentation – such as procedures, contracts and product sheets – RAG is the most suitable approach. It produces accurate answers without exposing the data to external training. A tailor-made AI application An RAG-based system can become a genuine business assistant grounded in the company’s actual data.
What the RAG does not resolve
RAG improves factual accuracy, but does not alter the model’s fundamental behaviour. If a company requires a model that adopts a very specific style, masters highly specialised technical vocabulary, or replicates lines of reasoning specific to a rare field of expertise, RAG alone may not be sufficient.
Fine-tuning: when deep specialisation is required
Le fine-tuning involves retraining a model on company-specific data to fundamentally alter its behaviour or knowledge. This is the most powerful – and most expensive – approach.
Use cases that warrant fine-tuning
Fine-tuning is appropriate when a company requires very specific behaviour: a precise writing style, specialised technical terminology (medical, legal), or strict format constraints on a large scale. Some AI agents in demanding contexts can be fine-tuned to improve their consistency when performing repetitive, high-value tasks.
What fine-tuning actually entails
Fine-tuning requires hundreds to thousands of annotated examples, computing infrastructure and technical expertise. The resulting model is fixed: if the data changes, it needs to be retrained. For the majority of SMEs, this level of complexity is not justified given the benefits offered by a well-designed RAG.
Combine the three approaches as required
In practice, the best AI solutions do not rely on a single approach: they combine them in a pragmatic way, depending on the requirements of each feature.
The most effective combination for an SME
The jumpsuit prompt engineering + RAG is the optimal balance: well-crafted prompts define the model’s behaviour, whilst RAG anchors the responses in real-world data. This is the architecture recommended by iterates for projects involving’integration of AI into business processes requiring precise and context-specific answers.
When to factor fine-tuning into the equation
Fine-tuning is used when the prompt + RAG combination does not produce a high enough quality of output: insufficient style, overly specialised terminology, or the need for high performance across a large volume of inferences.
Training staff to use AI effectively
Whatever technical approach is chosen, the quality of the results also depends on the teams’ ability to formulate clear instructions, evaluate the responses and identify instances where AI reaches its limits.
Developing a culture of prompt engineering within the organisation
An employee trained in prompt engineering produces significantly better results than an intuitive user. The AI training courses for businesses A series of workshops covers these practical skills to ensure that teams get the most out of the tools deployed.
Choosing the approach best suited to one’s level of maturity
A start-up would be well advised to begin with rapid engineering and a business chatbot a simple approach before investing in an RAG or fine-tuning. This gradual approach enables organisations to build an in-house AI culture and to scale their investment in line with the results observed.
Choosing the right AI approach with iterates
iterates supports Brussels-based SMEs with their AI strategy: from prompt engineering for initial use cases through to the deployment of RAG architectures and AI agents integrated into existing systems, based on their actual needs.
