What are Deterministic and Probabilistic Systems?
Deterministic systems are applications where the same input always produces exactly the same output—like a calculator that always returns 5 when you multiply 2 by 3. These traditional software systems follow predictable rules and logic paths, making them reliable and consistent. In contrast, probabilistic systems can produce different outputs even with identical inputs, dealing in likelihoods and patterns rather than certainties. Generative AI applications like ChatGPT or image generators are probabilistic—they use statistical models to predict and create content, introducing controlled variability that enables creativity and natural language understanding.
Since the dawn of the microprocessors and assembly language, 99% of all the applications we have been building and using fall in the category of Deterministic applications.
A Deterministic application is one where for a given input the output is always the same.
However with the dawn of Generative Ai, there will be a fundamental shift as a majority of the applications we will build and consume, will fall in the category of Probablistic Applications.
A Probablistic application is where for a given input, we could get broadly similar output but not necessarily the exact output for a given input.
The shift when moving from deterministic to predective applications
| Deterministic Apps | Probablistic Apps |
|---|---|
| Precision Focus: Every part of the system works exactly as expected. | Embrace Variability: Accept and plan for a range of possible outcomes. |
| Error Handling: Consider all possible errors and handle them predictably. | Statistical Reasoning: Use probability and statistics to understand and guide the system’s behavior. |
| User Expectations: Users expect 100% accuracy and reliability. | User Expectations: Users should be prepared for variability and understand that the application learns and improves over time. |
Embracing Uncertainty with Probablistic Applications
As Probabilistic applications incorporate randomness and probability theory to predict outcomes. They can produce different results under the same conditions, making them inherently uncertain. These applications have been prevalent for a while in complex systems like weather forecasting models, recommendation engines, data science, forecasting applications and now generative AI applications. Howerver the majority of us have not been building or using them unitl now i.e. the dawn of Generative AI applications.
Some of the immediate challenges we encounter when building Probablistic applications with Gen AI are:
Rethink our approach to UI & UX
- UI & UX designers will need to re-think the interfaces and navigation flows for GenAI applications. Merely sticking a chat infterface to the right or left of an exiting App will not cut it. Users will to be subtly prompted for the right kind of input prompts, Input guardrails will need to be put in place to ensure users don’t try asking irrelevant or harmful questions.
Designers will need to come up with creative ways to incorporate Reinforcement Learning from Human Feedback (RLHF) into the user interactions, so that the LLMs outputs get optimised.
As we move into multi-model Apps, it will be interesting to see how do we ensure our GenAI apps meet accessibility guidelines.
Rethinking our approach to Testing
Until now, our entire approach to testing has been to provide the system with a given set of inputs and ensure we have a consistent output. We write our test cases and use automation tools like Selenium etc., to run automated tests for deterministic outputs.
This approach will not work for testing GenAI applications. We will need a new set of tools or new ways to validate outputs that involve some amount of variance.
A common approach would be to check for different patterns and metadata of the outputs to ascertain a higher probability or accuracy score for the outputs.
Another approach would be to build a collection of QE agents or bots that perform the testing steps and then use inference to validate if the output feels accurate. We may need to ensure that the agents use different methodologies and perspectives, akin to a multi-faceted review system. This could involve incorporating varied testing paradigms and introducing stochastic elements into the test procedures to mimic real-world unpredictability.
Further, integrating a feedback loop where outputs are continually reassessed and the testing criteria are dynamically adjusted based on previous results could be vital. This adaptive testing strategy can help in dealing with the evolving nature of GenAI outputs, ensuring that our testing mechanisms remain robust and relevant.
Moreover, collaboration with domain experts to understand the nuances of expected outputs, and using their insights to inform our testing metrics, can provide an additional layer of validation. This human-in-the-loop approach ensures that the AI’s outputs are not just technically sound but also contextually appropriate and meaningful.
In summary, testing GenAI applications requires a paradigm shift from traditional methods. We must embrace complexity, adaptability, and a blend of automated and human-driven processes to ensure the reliability and relevance of GenAI outputs in real-world scenarios.
FAQ
What is the main difference between deterministic and probabilistic applications?
The fundamental difference is predictability. Deterministic applications always produce the same output for a given input—like a mathematical formula or traditional software. Probabilistic applications can produce different outputs for identical inputs because they use statistical models and probability distributions. This variability is what enables generative AI to be creative and natural, but it also requires different approaches to testing, user expectations, and system design.
Why are most traditional applications deterministic?
Traditional applications were built using imperative programming languages and algorithms that follow precise logical rules. This made them reliable, debuggable, and predictable—essential qualities for financial systems, databases, and critical infrastructure. The deterministic paradigm dominated because it matched how computers fundamentally work (executing precise instructions) and how humans expected software to behave (same input = same output).
When should I use deterministic vs probabilistic approaches?
Use deterministic approaches when you need exact, repeatable outcomes—financial calculations, database operations, control systems, or any situation where consistency is critical. Use probabilistic approaches when you need creativity, natural language understanding, pattern recognition, or when dealing with ambiguous, real-world data. Many modern systems combine both—using deterministic components for reliable core operations and probabilistic AI for flexible, intelligent behavior.
How do you test applications that produce different outputs each time?
Testing probabilistic applications requires different strategies. Instead of expecting exact outputs, you test for properties and patterns—checking that outputs are semantically correct, statistically appropriate, and within expected ranges. Techniques include using multiple test runs and statistical analysis, building evaluation frameworks that assess output quality rather than exact matches, creating AI-based testing agents, implementing human-in-the-loop validation, and measuring performance across many iterations rather than single test cases.
Do users accept variability in AI application outputs?
User acceptance depends on context and expectations. When users understand they’re interacting with an AI system, they generally accept some variability—especially in creative tasks like content generation or conversational interfaces. The key is setting appropriate expectations, designing UIs that acknowledge the probabilistic nature of the system, providing mechanisms for users to refine and iterate, and ensuring variability doesn’t cross into unreliability or errors.
How does UI/UX design change for probabilistic applications?
UI/UX for probabilistic applications needs to account for variability and guide users toward good inputs. This includes designing prompts and input mechanisms that help users provide effective context, incorporating guardrails that prevent inappropriate or harmful queries, creating feedback mechanisms (like thumbs up/down) that help improve the system, designing for iteration rather than one-shot perfection, and making the AI’s reasoning or confidence level visible when appropriate. Simply adding a chat interface to traditional apps isn’t enough.
Will deterministic applications become obsolete?
Not at all. Deterministic applications remain essential for critical infrastructure, financial systems, and any scenario requiring exact, predictable outcomes. What we’re seeing is the emergence of hybrid systems that combine deterministic reliability with probabilistic intelligence. For example, a banking app might use deterministic ledger processing while using AI for fraud detection, customer service, and personalized insights—each approach serving its appropriate purpose.
What skills do developers need for probabilistic applications?
Developers building probabilistic applications need skills beyond traditional programming. These include understanding probability and statistics, familiarity with machine learning concepts, prompt engineering and context design, designing effective evaluation frameworks, thinking in terms of likelihoods and confidence intervals rather than binary outcomes, and implementing feedback loops for continuous improvement. The shift requires embracing a new mental model while maintaining software engineering best practices.
About the Author
Vinci Rufus is a technology leader exploring the fundamental shifts in software development as AI becomes integral to application design. With extensive experience building both traditional deterministic systems and modern AI-powered applications, he helps organizations navigate the transition to probabilistic computing. Vinci writes about the practical implications of AI for developers, architects, and technology decision-makers.