When we talk about AI, we usually want something impressive: a model that surprises, generates unexpected ideas, and provides creative solutions. But there is a whole class of tasks where the opposite is required – for it to be maximally predictable, reliable, and, frankly, boring. The AI21 Labs team recently shared their experience working on such systems, and it turned out that creating a truly «boring AI» is no easy task.
Why Is a Predictable AI Model Necessary?
Why Do We Need a Predictable Model Anyway? 🤔
Simply put, there are scenarios where AI creativity is not an advantage, but a risk. Imagine a customer support chatbot that starts improvising instead of giving a standard answer. Or a document processing system that decides to «creatively reinterpret» data from a form. In such cases, the user needs not originality, but accuracy and consistency.
AI21 Labs worked on exactly this type of model – those that must perform a specific function without unnecessary liberties. And it turned out that achieving this is harder than it seems at first glance.
What Prevents Models from Being Predictable
What Stops a Model From Being Predictable
Modern language models are trained on huge volumes of text, and this gives them flexibility. They know how to adapt to context, generate diverse formulations, and find non-obvious connections. But that same flexibility becomes a problem when stability is needed.
In short, the model is prone to variability by default. Even with the same query, it can output several different answers – and this is normal for creative tasks. But for workflow scenarios where result reproducibility is important, such behavior is unacceptable.
Furthermore, models sometimes «invent» details that were not in the source data. This is called hallucinating, and in the context of, say, legal documents or financial reports, this can lead to serious errors.
How Models Are Trained for Predictability
How Models Are Taught to Be Predictable
AI21 Labs described several approaches that help make a model more predictable. One of the key ones is meticulous fine-tuning for a specific task. If a model is initially trained on everything indiscriminately, it needs to be additionally «grounded» in a narrow set of scenarios where it must work strictly according to rules.
Another important point is controlling generation temperature. This is a parameter that determines how freely the model chooses the next word. A low temperature makes the model more conservative; it chooses the most probable option more often. But even this is not a panacea: if the training data contains contradictions, the model might still remain unpredictable.
The team also emphasizes the importance of data quality. If the training set has examples with different answer styles for similar questions, the model will internalize this variability. Therefore, for «boring» tasks, very strict data curation is needed – selecting examples that are consistent and unambiguous.
Where Predictable AI Is Most Needed
Where This Is Really Needed
AI21 Labs provide several examples from practice. One of them is systems that extract information from text and convert it into a structured format. For example, resume parsing or processing applications. Here, the model must accurately identify the necessary fields and not add anything «of its own».
Another scenario is the automation of routine communications: order confirmations, notifications, and answers to typical questions. Here, it is not creativity that matters, but adherence to the template and factual correctness.
One more example is internal corporate systems that help employees quickly find information in a knowledge base. If the model starts to fantasize, it will undermine trust in the tool.
Why Predictability Is Difficult and What Remains Unresolved
Why This Is Difficult and What Remains an Open Question
One of the main complexities is that the industry focuses more on improving the creative abilities of models. Benchmarks, metrics, public discussions – all this is most often about how smart, diverse, and capable of reasoning the model is. But instruments for evaluating reliability and predictability are not yet plentiful.
Moreover, even with all efforts, it is difficult to completely exclude variability. The model still remains a probabilistic system, and in rare cases, it can produce an unexpected result. The question is how to reduce this probability to an acceptable level.
The AI21 Labs team notes that work in this direction continues. New control methods are needed, more accurate ways to evaluate stability, as well as a better understanding of how models make decisions in edge cases.
Why Is Predictable AI Important
Why This Is Important
At first glance, it might seem that «boring AI» is some kind of niche topic. But if you think about it, the majority of real tasks in business and everyday life require exactly reliability, not creativity. People need tools they can trust, which work equally well today and tomorrow, and which do not present surprises.
And if the industry wants AI to truly integrate into work processes, it needs to learn to create models that know how to be predictable. As it turned out, this is no less complex an engineering task than the creation of systems capable of writing poetry or generating code.
Simply put, sometimes the most valuable thing AI can do is just perform its work well. Without unnecessary frills, but stably and reliably.