In
this article, we’ll dive deeper into how GeneXus and LLMs think about software differently, and how we at GeneXus combine Certainty and Probability.

At bottom, the essential difference between expert systems like GeneXus and language models (LLMs) is not in the technological surface, but in the way they “reason” or “think.” It is the difference between deducing certainties from explicit facts and computing probabilities over implicit patterns.

1. The Deterministic Logic of GeneXus

The facts box and inference over certainties

From its inception, GeneXus was designed as a system based on explicit knowledge. Everything starts with the “base of facts”: facts and clear rules about how an application is built, how data is accessed, and what to do in each scenario. These facts are not an infinite list of possibilities, but a predefined and carefully curated corpus. It is knowledge built and accumulated over decades by the GeneXus R&D team.

• How does it work?

GeneXus generators work like logic engines. If certain conditions are met (“if this and this are true”), then new facts are deduced (“we can deduce that other thing”). It is classic logic programming: propositional rules where truth is chained and expanded step by step.

Each captured error, each new condition, is stacked as a new fact, and in this way the “knowledge base” grows.

• Where do the facts come from?

Some are contributed by the human who models the system. Others are embedded by design, the result of decades of accumulated experience.

• And what about ontologies?

On top of this base of facts, GeneXus can also build semantic relationships: ontologies that give the data a sense of “domain” (for example, in retail, talking about sellers, customers, etc.), which enables richer deductions and lets you model new complex scenarios without breaking the logic.

The key is that everything that is deduced is deduced with certainty and with clear rules. There is no “probability.” If it is in the base, it is because someone – a human or the GeneXus team itself – validated it as true. This makes the platform ideal for systems where the margin of error must be minimal: banking, healthcare, government, mission critical.

2. The Probabilistic Approach of LLMs

Patterns over an immensity of implicit data

LLMs (Large Language Models) operate differently, practically in another dimension. They are not limited by a finite base of facts.
In
stead, they are “trained” on massive amounts of implicit information, far beyond what any team could compile explicitly.

• How does it work?

LLMs look for patterns. Their task is to predict, given a context, what the most likely next element is. While they can deduce facts and relationships, they do so from millions of examples and connections, not from formal certainties, but from correlations and probabilities.
In
essence, they do not reason. They predict.

• What does this imply?

The power of LLMs lies in their coverage. They can infer novel connections, adapt to unforeseen cases, and suggest plausible answers even in unfamiliar contexts.
In
other words, they improvise and can even hallucinate.

• What is the trade-off?

Their answer is always a probabilistic bet.
In
domains where some margin of error is tolerable – creativity, comprehension, sentiment analysis, conversational automation – their versatility is unsurpassed. But in systems where “exactly correct” is crucial, their uncertainty can be a problem.

3. A Difference That Complements

Truth as architecture or as exploration

GeneXus builds from explicit “truth”. The result is predictable, verifiable, and secure. LLMs explore the space of what is possible. They are often right, sometimes wrong, but their frontier is much wider.

GeneXus:

• Automates what is already known to be correct.

• Evolves with the knowledge base and the ontologies you create.

• Minimal margin of error, ideal for enterprise systems, though limited by what is modeled.

LLMs:

• Powerful for navigating the unknown or the non formalized.

• Capable of discovering invisible patterns.

• Always take a small risk. Sometimes that enables innovation, sometimes it yields errors.

We have long worked with the vision that it is not about choosing between certainties or probabilities (between symbolic, deterministic AI or generative AI), but about understanding that both perspectives are needed and reinforce each other. That complementarity is what is allowing us to build increasingly powerful platforms, with a unique differentiator that is hard to replicate in the market, because it rests on 35 years of experience and on GeneXus technologies based on symbolic AI.

4. The Future: Convergence and Symbiosis

Today, the strategy of GeneXus Next – and even of Globant Enterprise AI – is based on integrating both logics and intelligences:

• Use logical inference and deterministic generators for what is critical.

• In
  corporate generative assistants (LLMs) for modeling, UX, and “intelligent” automation.

• Enable the creation of new agents and agentic processes to solve challenges that were – or seemed – impossible to solve.

• Work on explicit facts while also leveraging the flexibility of probabilistic models for the tasks in which they excel.

Convergence is not only about technology, but also about philosophy:

• GeneXus starts from what you already know and ensures the result.

• LLMs start from everything that “could be” and open possibilities.

The key for the future is to understand that they are two different types of tools, and that there is great value in knowing when to trust each approach and how to make them work together so that they amplify each other. It is not about choosing certainty or probability, but about combining both to get the best of two worlds: continuity in what is critical and innovation without limits. That complementarity is what opens the path to stronger and broader platforms, like the ones we are building with GeneXus Next and Globant Enterprise AI.

You may also be interested in reading:

GeneXus vs Vibe Coding, Knowledge vs Prompting

GeneXus Next: Native Agentic Low-Code Development for Mission-Critical Systems

Creation and
In
novation in the Age of AI

Low-Code + Generative AI: Challenges and Opportunities for CIOs