Probabilistic vs Deterministic Retrieval

You’ve probably heard of these terms before, one is used to characterize the behavior of LLMs, and the other that combats the creative tendencies of the LLM. But what does probabilistic retrieval mean in the context of GenAI and how does deterministic retrieval improve the quality of responses generated by the LLM?  

Let’s start with the basic definitions. Probabilistic in simple terms means based on or related to how likely it is that a specific event will occur. Deterministic implies that all facts or events are determined by external causes and follows natural laws. How does that translate to the context of GenAI? Why should anyone building GenAI applications be concerned with these types of retrieval?

In AI and Machine Learning, Probabilistic and Deterministic models are popular and expand into the field of deep learning. What we consider hallucinations in Generative AI is purely an outcome of the probabilistic nature of the LLMs. By claiming that LLMs are probabilistic the idea is that they process inputs which are prompts and pull out a string of responses, either words, numbers images and the likes. All thanks to the algorithms used to train the data and the corpus of information it’s been trained on.

So, when you say probabilistic in a GenAI context, it implies that the LLM is fetching the next token, and the basis of getting that next token while impressive at scale with reinforcement learning, it theoretically is an approximation on what the best next token could be. So, with that reference you get something can speak well, but it's clearly not understanding.

Having said that a probabilistic approach has other applications where it can be extremely useful. Probabilistic modeling is a statistical approach that uses the effect of random occurrences or actions to forecast the possibility of future results.  This method is used quite extensively for predictive AI applications.

It isn’t likely that the same architecture that works in the conversational aspect will work to understand. It will be part of it. An analogy would be if you think of your own brain. You have an interface that is interrelated and overlapping with the speaking and thinking parts. But in a sense, they are also separate.

In the context of your GenAI applications, you want to get your system to give you the best possible data and therefore you want a very reliable, what we call deterministic way to point out why is it that this query from the user retrieved these specific data points. What is it that it thought? What was it that was interrelated in that ontology? What was it that was inferred? Not necessarily because it was in a dense embedding space, but because you had already put a certain implication chain that A implies B, implies C. What are some of these things that finally gave us a set of data points which have their attributes and are enriched? This then enables you to reason out why that specific response was generated and further use this retrieved data by using a template, a small language model, or a large language model to get your agent to do, to respond or carry out a specific function based on it.

To sum it up, the distinction between probabilistic and deterministic retrieval methods in Generative AI (GenAI). Probabilistic retrieval, which relies on predicting the next token in a sequence, excels in generating fluent text but may lack deep understanding and traceability. In contrast, deterministic retrieval focuses on clear, explainable connections between data points using structured ontologies or predefined logic chains. Developers working with RAG or building GenAI applications should aim for a balance—leveraging probabilistic methods for conversational fluidity while using deterministic retrieval for reliability, accuracy, and explainability in responses.

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