Imagine a company swamped with thousands of customer messages handled solely by customer support teams. Truth being told, one does not have to imagine, as we’ve all been there— either as employees or customers awaiting responses that should have been provided off the cuff. Luckily enough, with the rise of smart solutions quickly sorting out & understanding all the customers’ messages — and even responding to each customer personally — we’ve found a threshold to our ultimate live chat experience.
This giant communication leap has been made possible by introducing an advanced Large Language Models (LLMs), a type of smart computer programs that can read and write like a human. Thus, we all can already feel the power of AI, how it significantly boosts handling customer feedbacks and increases customer happiness. And it’s just one tiny use case taken from the tip of a very steep and mammooth — size iceberg.
What we are witnessing here will inevitably change our lives’ tectonics. The sooner you and your business jump on the AI wagon, the better off you will be, so let’s take a glance at what led us here & how yesterday’s fantasy became today’s reality — all thanks to the power of Large Language Models.
The journey into LLMs reveals their potential to revolutionize not just customer interaction, but the entire business landscape. These models go beyond handling feedback - they unlock new opportunities for strategic innovation and market insights. By analyzing data and generating creative solutions, LLMs will help businesses tailor their offerings, predict trends, and plan all the business operations. This tremendous potential is already being unlocked by the trailblazing tech companies, and the epigones will walk the same line, opening the whole tech industry to new ways of thinking and competing in the digital age.
Truth being told, the tech industry had already been working on models and techniques for processing human language since it picked on the ideas of neuro-linguistic programming (NLP) from Richard Bandler and John Grinder’s 1975 book “The Structure of Magic I”. Why are LLMs so special, then? And, whence their gravitational power attracting all of us?
In a crude nutshell, it all boils down to mechanisms that are getting us uber-close to human cognitive attention capabilities. This magic within LLMs is made possible thanks to their internal information ’deep learner’ called the transformer architecture. Think of it as the brain behind the AI, making sense of words not just by looking at them in isolation, but by considering the whole sentence & calculating weights for each word, and how they fit in particular sentence’s context window.
This allows LLMs to pick up on the subtle nuances of language, much like how we understand that the word “bank” can mean different things depending on whether we’re talking about a river or money. Seems simple, right? Let us plunge tad deeper, then…
Large Language Models start learning by reading lots (and lots) of text from websites, books, and articles. Think of this first step as their basic training, where they learn all about language itself. After getting the basics down, they receive special training for specific jobs. e.g. helping with customer questions or creating new content. This makes them super flexible and able to do many different tasks.
Each piece of linguistic data such model encounters is then processed by the already mentioned transformer architecture, which comprises two main parts: one that breaks down the text into smaller pieces, and another that looks at these pieces to figure out how they all fit together.
What makes the transformer architecture truly special here is its ability to focus on dependencies and relationships within input data sequences, which makes the model aware of what’s ‘between the lines’. Attaining such cognitive self-attention is indeed a giant leap in neuro-linguistic programming, as it allows the model to see patterns much like we do when we read. Think this; just like a forest is not mere multiplication of trees, but a whole living ecosystem, natural languages are living systems with all their intricacies and liminal connections. And that’s what we are able to specifically tap into these days with the introduction of transformer architecture in LLMs. Big thing.
Basics covered. Let us peel the onion and unravel more details below.
Large Language Models (LLMs) are built using specialized & multilayered neural networks, each playing its own part in understanding and creating language. These models have different types of layers, such as feedforward, embedding, and attention layers. Together, they process the words we feed them with and generate meaningful responses:
However, the diversity of LLMs extends beyond their architectural design. These models are further distinguished by their specialized training and application domains, leading to different “flavors” of LLMs:
Seems complex, right? Luckily, understanding every technical detail isn’t necessary to leverage the power of LLMs. Next, we’ll see why training your own LLM might not be needed, easing your journey into utilizing these transformative technologies.
The short answer is no*. Training a Large Language Model from the ground up requires substantial resources, including a vast dataset and significant computational power, not to mention the expertise to manage the training process. Fortunately, the development and accessibility of LLMs have reached a point where businesses can utilize pre-trained models for a myriad of applications without the need for in-house AI training capabilities.
…for you it does look a bit different, as being in your shoes requires dropping a handful of baseline questions in the areas of:
Security: How to protect your LLMs from unwanted data ingestion and how to avoid a state in which your models will be learning on tarnished data, thus presenting strongly biased outputs? Which LLM will be customizable enough to work solely on your organisation’s data & behind your firewalls?
Privacy: e.g. in the context of Compliance — think SOC 2 & ISO auditors asking you about the risk of LLMs ability to pick data from your business’ internal prompts & potentially disclosing that information to other parties querying for related things. And no, *yikes!* would not be the correct answer. Or, will the Service Provider guarantee the LLM you are about to introduce will not morph further utilizing bits of your data? The list goes, unfortunately, on, but there are ways to address it.
Data Governance: does your organisation have a centralized data source? How do you make sure that data is brought into it and governed accordingly? Furthermore, what kind of data source is essential to train your model, and how do you divide it in data sets?
In an nutshell, the issue with the pre-existing models boils down to at least being aware of the fact, that in some contexts you will be risking having your proprietary data pushed into publicly hosted LLMs.
All right, enough of talking the talk, at the end of the day you want to start utilizing the potential behind the LLMs and AI itself, and want to start training your own model, right? Assuming you are aware of the high-level business requirements, the security & compliance bottlenecks, you should be good to go…
…but please hold your horses for a sec, and think about one missing bit in the whole story. The very baseline. Your data.
Based on what we keep seeing in terms of companies’ AI readiness, we can’t stress enough the critical importance of thorough data readiness in the areas of:
The objective of these steps is not just cleaning or organizing data, but transforming it into a powerful tool for AI models to learn from and interact with effectively. Additionally, by ensuring the infrastructure is ready, we pave the way for easy integration and operation of AI technologies, making the data not just clean and structured, but also primed for advanced computational tasks. A must have.
This approach ensures that solutions are not only effective but perfectly matched to the unique challenges and goals of each initiative.
This aims to simplify the process of AI integration, turning it into a clear, manageable step in enhancing workflows and underscoring the practical advantages of incorporating AI.
The need for huge resources to use AI is gone. Now, businesses can innovate and improve with pre-trained AI models like never before. But, let’s ask ourselves: are we ready for AI? Understanding your real needs without emotional bias is one thing, but what about — yes, data. Are you really ready?
In our next pieces of writing, we’ll look closely at AI data readiness itself. Our intention is to take a deep dive on all the facets of building robust data architecture that will ensure your AI projects’ swift take off.
Stay tuned for updates and tips on using AI and making sure you’re ready for what’s next in innovation! 🙂
At insightify.io, our expertise lies in assessing and preparing infrastructure for AI integration. From AI readiness checks to ensuring systems are primed for LLMs, we guide organizations through every step of the journey towards AI adoption.
If you’re considering harnessing the power of AI and want to ensure your infrastructure is up to the task, reach out to us for more information!