FM and LLM Observability: The key to measuring user impact

Align the performance of FM (acronym for “foundational models”) and LLM (acronym for “large language models”) with business needs. This objective is achievable thanks to observability: an evolution of monitoring with special emphasis on business impact and users. Especially in these times when companies are looking to get more value from generative AI.

Both FMs and LLMs are machine learning-based models. As such, it is to be expected that their behavior is not deterministic. This means that the same input value (or stimulus) may produce different answers or results at different times.

When developing, testing and making available this type of solutions, therefore, special care must be taken in the model outputs (the responses or general content). This is because such variations may affect the users. Observability applied to FM and LLM, therefore, offers the possibility to monitor the performance of the models. But, much more importantly, to measure the impact on users.

Why is it important to apply observability in FM and LLM?

From what has been explained above, it is clear that the fact that a model generates a response does not necessarily imply that it is in line with what we are looking for. 

In this situation, organizations must develop the ability to understand how users perceive the content they receive from these models.

At the same time, the need to make responsible use of the models is an increasingly popular requirement. This, from the point of view of an organization implementing a solution based on FM and LLM, implies many things. Among them, detecting responses and content that have been generated that are not within the parameters acceptable to the company.

Organizations must be able to answer these questions:

– Are the responses or content generated by my models getting good feedback from users?

– Is the intended business mission being achieved through the use of these models?

Metrics for ensuring FM and LLM performance

All the metrics of machine learning models in general and natural language processing models in particular apply. But, in addition, specific metrics should be incorporated such as:

– Coherence.

– Fluency.

– Data-driven responses (groundness).

– Relevance.

– Retrieval score (in cases of implementing RAG -retrieval augmented generation-, the relevance of the retrieved document is measured).

– Similarity.

– Security aspects (e.g., hate speech or sexual, violent or self-harm related content).

The benefits of applying observability on FM and LLM

The application of observability on FM and LLM qualifies as a real business need. Here are some of the main reasons:

Decreased associated risks

Releasing a solution that uses an FM or LLM in the background without observing and monitoring its behavior involves numerous risks. From hallucinations and inappropriate responses to poor quality content that may generate reputational damage or wrong decisions.

Ensuring safe and ethical responses

This is in line with the above, and again with the focus on the responsible use of these types of tools. Observability in FM and LLM ensures safe and ethical responses. This means that they do not contain or include hate speech, elements of discrimination or racial bias. Nor sexual, violent, self-harm related or any other content that may affect the user or ultimately the business.

Application and monitoring of safeguards

Applying safeguards and monitoring that the responses generated are within the limits of these safeguards is fundamental to ensure a solution that includes Generative IA components.

Why Nubiral

At Nubiral we bring together talent and experience in both observability and machine learning models, highlighting our Center of Excellence (CoE) specialized in Generative AI.

A combination of competencies that guarantees your organization the best results.

Conclusions

The interesting thing about observability is that, unlike monitoring, it does not focus only on technical aspects, but mainly on the impact on the user. A model can respond very quickly, but if its response is not commensurate, for whatever reason, it is not going to be a quality response. 

This is the “nitty gritty” of observability in FM and LLM. Ensuring that the responses and content that the end user receives is compliant, consistent, contextualized, secure, relevant and data-driven.

In other words: observability is a key part of driving all the benefits that these solutions propose while keeping risks to a bare minimum.

Interested in getting started right now to learn how your FM and LLM models impact your users? Our experts look forward to hearing from you. Schedule a meeting!