"Exploring what should organization considering when choosing to adopt LLMs" with guest Nick Brady, Senior Program Manager at Microsoft Azure Open AI Service

AI has been at the forefront of transformation for more than a decade now. Still, the Open AI launch of chat GPT in November 2022 will be noted as a historical moment – the scale of which even Open AI did not expect – in the history of technological innovations. Most people don’t realize or fully appreciate the magnitude of the shift that we’re in. Now, we’re able to directly express to a machine a problem that we need to have solved; equipping these technologies with the right reasoning engines and the right connectivity could bring the biggest technology leapfrog not just for enterprises but even in everyday lives.

The onset of leapfrog does bring out a few questions for enterprises looking to adopt GenAI as a part of their strategy, operations and way ahead, like:

if we equip these technologies with the right reasoning engines and the right connectivity to this work and the things that we do every single day, this could be the single most democratising force of technology like the world has ever seen, not just in enterprise but even in our personal lives.rn

– Nick Brady

What use cases are best suited to adopt the models?

While most customers are looking for how this could reduce business costs in their organizations, the true value is when it is used to maximize business value productivity and downstream that could lead to employee satisfaction and customer satisfaction. Any place where there’s language – programming or natural language – is a good use case for generative AI, and that probably would be the most profound shift. So, if you have language, if you have a document, if you have big data where you’re trying to sort of synthesize, understand what that content and what the content is, generative AI models can do this ad nauseam without any delay.

The interesting thing about parameters is it’s actually not a direct correlation to how powerful the model might be. I mean, the parameter size refers to essentially the number of values that the model can change independently as it learns from data.

– Nick Brady

The most common metric used across the world to describe LLMs is the number of parameters; in the case of GPT 3, it is trained on 175 billion parameters, but what does this mean?

Parameter size refers to essentially the number of values that the model can change independently as it learns from data and stores all information in the vast associative ray of memory as its model weights. What’s perhaps more important for these models, and it speaks to more of their capability, is their vocabulary size.

How does one decide and evaluate which would be the best-suited model for the selected use cases?

The best practice really is to start with the most powerful and advanced language model like GPT 4.0 to test, if it’s even possible, with your use case. Post confirming the possibility of use case trickle down to simpler models to find its efficacy and efficiency. If the simpler model can probably achieve 90% of the way, with just a little bit of prompt engineering, then you could optimize for costs.

But many of LLMs are English only and that’s a real problem, especially for multinational enterprises and organisations that have diverse employees and diverse customers that speak in many different languages.

– Nick Brady

Organizations would have to define what quality means to them. It could be the model’s output, its core response, or performance in terms of latency, where the quality of the output may not be as important as how quickly we can respond back to the user.

The key for leaders is to pay close attention to the potential use cases, test them with the best model and then optimize the model to balance the cost, efficacy and efficiency factors.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

Join us in this episode featuring Anupam Datta, Co-founder and Chief Scientist, TruEra, as we dive into the evolution of LLMs and what they hold for the future!

This world of generative AI has caught us by storm. And as enterprise leaders in your companies, understanding the technology behind generative AI will give you a competitive advantage as you plan your companies and businesses. And to help you do this, we will unpack a technology, large language models (LLMs), that powers AI today and represent a paradigm shift in this field of Artificial Intelligence.

LLMs can craft meaningful responses across many domains. Their performance has notably improved recently thanks to the substantial increase in model size and data volume.

With the increasing acceptance of this technology, numerous companies are unveiling various Large Language Models (LLMs). It's important to recognize that opting for the largest or highest-performing LLM isn't always the most suitable approach. Instead, one might prefer LLMs that excel in specific tasks relevant to their application. As a leader in the enterprise, it's crucial to integrate this understanding into your company's strategy, aiding in identifying the appropriate LLMs to match and adapt for your applications. Achieving equilibrium between LLM selection, cost considerations, and latency considerations stands as a pivotal concern for enterprises. Equally essential is the thorough validation and assessment of generative outputs, serving as a safeguard prior to embarking on consequential choices. Hence, the undertaking of reliability testing at this current juncture is paramount.

Furthermore, enterprises need to consider a few other key aspects in this evolving landscape of LLMs as they build out LLMs. Starting with a well-defined business use case that offers real value is crucial. As LLMs move from development to production, it's important to establish thorough evaluations and observability throughout their lifecycle. Education across the organization is vital to implement LLMs effectively. Companies should train their workforce to adapt to this changing technology stack. Fostering a community around responsible AI development and evaluation can contribute to a better understanding and management of LLMs. With these steps, enterprises can navigate the complexities of LLMs and harness their potential for positive impact.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

Join us in this episode featuring Ankur Taly, Staff Research Scientist, Google, as we explore the concept of grounding of LLMs!

Machines are supposed to work without mistakes, just like a calculator does math correctly. But in the world of artificial intelligence, errors, often called 'AI hallucinations,' are common. This makes us wonder about these mistakes and the computer programs behind them. For businesses that use AI in their work, especially when dealing with customers, making sure AI works without errors is very important.

Understanding how AI makes decisions and being clear about its processes is very important. Business leaders need to be able to watch and explain how AI makes decisions. This will be crucial for using AI in their companies in the future.

To fight AI hallucinations, grounding is important. Grounding means making sure AI answers are based on real facts. This involves teaching AI systems using correct and reliable information and making them give answers that can be proven. Grounding stops AI from making things up or giving wrong information.
When businesses use LLMs (large language models) in their work, they should think about some important things. First, they need to use good data to teach AI because bad data can lead to wrong or unfair results. It's also important to have rules about how AI is used in the company to avoid causing harm or misusing AI information.

Businesses also need to keep an eye on AI's results to fix mistakes or wrong information. Having people check and filter AI's work ensures that it's correct and consistent. It's also important to teach employees and users about what AI can and can't do to avoid misunderstandings or misuse.

Even though AI hallucinations can be a problem, they can also have some positives. They can make people think creatively and find new solutions to tough problems. AI's imaginative ideas can be fun, offering new types of art and media. Plus, AI hallucinations can help with learning by making people think and talk about interesting topics.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha