Quentin Fournier
Artificial Intelligence (AI) is reshaping the way businesses operate. Tools powered by Large Language Models (LLMs) such as ChatGPT, Claude, Gemini, or Llama are helping companies draft reports, analyze markets, answer customer questions, and even generate code.
Yet, despite their power, these models share a common limitation: hallucinations. An AI hallucination happens when a model produces information that looks convincing but is simply wrong.
In personal use, this may be harmless. In business, it can cause real damage—misleading a sales forecast, distorting competitor insights, or introducing compliance risks. That’s why professionals must understand what hallucinations are, why they occur, and how to avoid them when deploying AI at scale.
An AI hallucination happens when a model generates information that is false or fabricated, but presents it with full confidence. The AI isn’t “lying” on purpose—it simply tries to fill in the gaps when it doesn’t have enough relevant context to provide a correct answer.
This problem is tightly linked to the concept of context windows—the AI’s short-term memory. Every conversation or uploaded document is converted into tokens (pieces of words). The model can only keep track of a limited number of tokens at once. When that limit is reached, earlier information is pushed out and “forgotten.”
The result: the model may invent details to bridge the missing pieces, which appear as hallucinations.
📌 Example:
Imagine you’re chatting with an AI and you tell it at the very start, “I’m currently reading a book called How to Take Smart Notes.” The conversation goes on for a while—you ask for a story about cows, then a sequel, then a prequel. By the time you come back and ask, “What book am I reading right now?” the AI has already forgotten. Its short-term memory (the context window) is full, and that first detail has been pushed out. To avoid leaving a gap, the model may try to guess—perhaps saying you’re reading a completely different book, even though it sounds confident. That’s an AI hallucination: a confident but false answer created when the model no longer has access to the right context.
Think of the context window as the AI’s short-term memory. Every interaction—whether a question, a chat history, or a document upload—is stored as tokens (fragments of words).
Each model has a maximum number of tokens it can “remember” at once:
Once the conversation goes beyond this limit, the model begins to forget earlier parts of the exchange. If you want to learn more about tokens, you can read our article on Artificial intelligence and tokens : Read our article
📌 Analogy: Imagine a long meeting with a colleague. At first, you both remember every point. After hours, you start forgetting what was said at the beginning, and eventually, the discussion loses focus. LLMs experience the same problem when their context window is overloaded.
Even with larger context windows, hallucinations still occur. Research called Lost in the Middle has shown that LLMs tend to remember the beginning and end of a document better than the middle. Accuracy follows a U-shaped curve: strong at the edges, weak in the center.
This means that even if you upload a 200-page contract, the AI might summarize the introduction and conclusion correctly but misinterpret (or forget) critical clauses hidden in the middle.
Another technical factor is attention mechanisms. To decide which words are important, the model assigns attention scores. In short conversations this works well, but in very long ones, the calculations become complex, slower, and less precise—opening the door to hallucinations.
📌 Example from business use:
For enterprises, hallucinations are more than a technical curiosity—they are a business risk.
If AI is to be a reliable co-pilot, companies must adopt practices that minimize these errors.
The good news: hallucinations can be reduced. Businesses that combine better prompting with direct connections to their own data sources see more reliable results.
AI works best when guided by clear, structured instructions.
📌 Example:
A marketing manager asking:
“Analyze these 3 competitor campaigns. First, list their channels. Then suggest 5 unique strategies for us.”
… will get far more accurate output than simply asking: “What are competitor strategies?”
💡 Pro Tip for Businesses: Instead of rewriting this prompt every time, create a dedicated AI agent for competitor analysis. By attaching a predefined prompt, the agent can always follow the same structured steps—ensuring consistency across the marketing team.
This approach not only reduces hallucinations but also:
Prompting is important—but the real breakthrough comes from grounding AI in your company’s data.
When LLMs rely only on their pre-trained knowledge, they guess. By connecting them to live, verified sources, you eliminate much of that guessing. This approach, often called Retrieval-Augmented Generation (RAG), ensures answers are based on facts.
When it comes to text generation, grounding the model in business data changes everything. Instead of producing generic or speculative answers, the AI can generate content that is accurate, contextualized, and directly relevant to your organization. For instance, a connected model doesn’t just “invent” a sales report — it builds one from the real numbers in your CRM. A customer email draft isn’t filled with vague product descriptions — it pulls the exact details from your knowledge base. This connection makes the AI less likely to hallucinate because every sentence it generates is anchored to verified information rather than guesswork.
Examples of data sources to connect AI with:
📌 Analogy: Imagine you hire a new employee but don’t give them access to past reports, customer files, or internal tools. No matter how smart they are, they’ll end up guessing and making mistakes. LLMs work the same way—without being connected to your data, their performance quickly drops.
Business use case:
See one of the internal use cases when we had to work on our internal Culture :
Hallucinations are part of how LLMs work—they don’t mean the technology is failing, but they do show the limits of memory and attention. In business, though, leaving hallucinations unchecked is too risky.
The most effective way to reduce them is to connect AI directly to trusted company data. Grounding the model in real information keeps outputs accurate, consistent, and relevant. Combined with clear prompting and a final verification step, this turns AI from a trial tool into a reliable business partner that supports decisions and builds trust.
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