gensai.ai
Ask an artificial intelligence the same question several times and the answers can vary. Sometimes the difference is minimal, a simple rewording. In other cases, the recommendations themselves shift slightly.
This can be surprising, especially in a professional context where software is generally expected to produce the same result every time.
To understand this behaviour, we need to go back to how generative AI models such as LLMs (Large Language Models) work. Unlike traditional software, these systems rest on probabilistic mechanisms that introduce a degree of variability into their answers.
Why doesn't an AI work like a classic search engine?
A classic search engine works mainly by indexing and retrieving information. When a user submits a query, the system looks for the most relevant documents in a database and displays them.
Generative AI models work differently.
They do not simply retrieve information: they generate an answer word by word, based on the probabilities learned during training.
When a question is asked, the model calculates:
- the probability of the first word of the answer
- the probability of the next word
- and so on until a complete text is produced
Several words can be plausible at each step. The model therefore has to choose among several possible options.
It is this probabilistic logic that explains why two answers can differ while both remaining relevant.
Why can answers vary for the same question?
Several factors can explain these variations.
1. The probabilistic nature of the models
LLMs do not always select the most probable word. They often choose among several possible options in order to avoid overly repetitive answers.
Two successive runs can therefore follow slightly different paths through the text generation.
2. Generation parameters
AI systems use various parameters that influence how answers are generated.
For example:
- Temperature: controls how creative the model is
- Top-k: limits the selection to the most probable words
- Top-p (nucleus sampling): dynamically adjusts the candidate words
A high temperature increases the diversity of answers, while a lower temperature produces more predictable ones.
3. The conversation context
Conversational models take previous messages into account.
Two identical requests can therefore produce different results if:
- the conversational context changes
- the instructions given to the model evolve
- different documents are used as sources
In modern architectures, particularly those using RAG (Retrieval-Augmented Generation), the answer also depends on the documents retrieved at query time.
Why do we sometimes talk about AI "hallucinations"?
Another well-known phenomenon of generative models is hallucination.
A hallucination is a situation in which the AI produces information that is:
- incorrect
- approximate
- or entirely invented
Unlike a classic error, the AI can present this information with a great deal of confidence, which can give the impression that it is reliable.
The phenomenon is largely explained by the probabilistic workings of the models.
An LLM aims above all to produce a linguistically coherent answer, even when it has no reliable information on the subject.
In that case, it can combine plausible elements to produce an answer that seems credible but does not match reality.
Why do hallucinations happen?
Several factors can encourage hallucinations:
1. A lack of reliable data in the context
If the model has no precise information on a subject, it may generate an approximate answer.
2. Ambiguous questions
A poorly formulated or overly vague question can push the model to interpret the intent of the request.
3. No structured sources
Without access to a knowledge base or verified documents, the AI relies solely on its linguistic probabilities.
4. Temperature set too high
The more the model is configured to be creative, the more it can produce original — but also imprecise — answers.
How can you reduce the variability of an AI's answers?
In a professional context, more consistent and reliable answers are often required.
Several approaches help reduce this variability.
Adjust the model's parameters
Lowering the temperature and adjusting generation parameters produces more deterministic answers.
Structure the prompts
Precise instructions help steer the model's answers:
- specify the expected role
- indicate which sources to use
- ask for answers to be justified
Use knowledge bases (RAG)
RAG architectures make it possible to connect models to internal documents or databases.
The AI no longer relies solely on its linguistic probabilities, but on real information extracted from documents.
Put human validation in place
In critical uses (legal, medical, financial), generated answers must be validated by a human expert.
The AI then becomes an assistive tool rather than an autonomous decision-making system.
Should you distrust an AI's answers?
Artificial intelligence models are powerful tools, but they work differently from traditional software.
They rest on probabilistic models able to generate several plausible answers to the same question.
This characteristic explains all at once:
- the richness of the answers produced
- the variability observed
- and the possibility of hallucinations in certain cases
Understanding how this works is essential to using AI effectively.
Organisations that get the best results with AI generally combine several approaches:
- structuring their data
- using reliable knowledge bases
- configuring models appropriately
- human oversight
The goal is not to eliminate model variability entirely, but to channel their generative capability in order to obtain useful, relevant and reliable answers in a professional context.