The Complete WAKIB Glossary: How to Speak AI Without Sounding Like a Beginner

Artificial intelligence is no longer a niche technical subject. It has become part of everyday news and public conversation. As it has spread, a new set of terms keeps appearing in reports and social media posts, almost forming a "new language" that ordinary readers need to understand in order to grasp how these systems work, what their limits are, and where common misunderstandings arise. Below are the most important of these terms, arranged according to how often readers actually encounter them in the news, not alphabetically.

How to Talk to Artificial Intelligence

The first thing any user encounters when dealing with an AI model is the concept of the prompt, the input or instructions given to the system to carry out a specific task. It can be a simple question or a detailed command. When you type "translate this English text into French," that request is the prompt. Users write in their normal language, and the model responds, summarizes, translates, or writes code depending on the nature of the request. A common mistake is assuming a prompt is just a random question with no real difference between versions of it. In fact, how precise and detailed a prompt is directly affects the quality of the answer: a specific, detailed prompt produces better results than a vague one.

Another capability frequently mentioned when discussing answer quality is Chain of Thought, the model's ability to break a complex problem down into smaller logical steps before reaching a final answer. Language models use this approach to improve the quality of answers to logical or programming problems, even though it takes more time. A common mistake is confusing this capability with "actual thinking." The model does not think in the philosophical sense; it follows a specific pattern of producing sequential steps that resemble reasoning without being genuine reasoning.

Problems Repeatedly Mentioned in the News

One of the most common terms that comes up when discussing AI errors is hallucination, a situation in which the model produces false or incorrect information with complete confidence, as if it were established fact. One reason for this is that most models have a "knowledge cut-off date," meaning they have no information about events after a certain point. Asking about something that happened yesterday may push the model to guess if it is not connected directly to the internet. A common mistake is believing hallucination only happens with weaker models, when in fact even the most powerful models hallucinate sometimes. The solution is not to wait for a perfect model, but to have humans verify sensitive information before relying on it.

There is also the term bias, which occurs when the data used to train a system is not representative or includes fragmented, inaccurate information, leading to unfair decisions. For example, an AI system used in hiring that is trained on historical data showing most executives are male may end up biased against female candidates. A common mistake is confusing "bias" with "discrimination." Bias is a problem related to the data and the model itself, while discrimination is the actual act of unfair treatment; the first is technical, the second is legal and social.

Beyond the Chatbot: Agents and Customization

The term agent comes up frequently today, referring to a system capable of stepping outside the framework of a text conversation to interact with other programs, such as opening an Excel file, sending a message through a work app, or retrieving data from an administrative system. The difference between an agent and a traditional chatbot is that the chatbot only answers questions based on information it has, while the agent plans and carries out actual actions in other systems, such as booking an appointment or issuing an invoice. A common fear associated with this term is that agents will completely replace employees, when the stated goal is actually to enhance an employee's capabilities: the agent handles routine and complex research tasks, freeing the employee to focus on strategic and creative decision-making.

Another common technical term is fine-tuning, which means retraining an existing model using additional specialized data related to a particular task or field. A company that sells sports equipment, for instance, might use this kind of refinement to make a model respond better to questions about maintaining an exercise bike. A common mistake is thinking fine-tuning means building a model from scratch, when in reality it simply improves an existing model by adding specialized knowledge.

Major Terms That Need Careful Scrutiny

The term compute appears when discussing the costs of developing AI, and refers to the computing resources needed to run and train models. A common mistake is failing to recognize that a model's quality is directly tied to the scale of these resources; a given model's weakness might stem from the amount of resources invested in it rather than a flaw in its design alone.

Finally, the term Artificial General Intelligence (AGI) dominates headlines, referring to systems that surpass humans in most tasks. However, definitions of it vary widely: some view it as a system equivalent to an average human that could be hired as a co-worker, while others see it as a system that outperforms humans in most economically valuable work. A common mistake, one that even experts sometimes make according to various reports, is the absence of a precise, agreed-upon definition for this term. So when it comes up in the news, it is worth remembering that it refers to an extremely powerful system, not a physically existing humanoid robot.

Understanding these terms is no longer limited to developers and researchers. It has become essential for ordinary users too, since these terms explain how the applications they rely on every day actually work.