Large Language Model Fundamentals and Leveraging AWS

DoiT International’s Eduardo Mota offers insight on large language model fundaments and leveraging AWS. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Large language models (LLMs) have become all the rage. Talk to anyone working with artificial intelligence (AI) and you’ll likely hear a new universe of names, including Claude, Cohere and Llama2. Setting aside the hype, what exactly are LLMs? How are they used to create powerful AI applications? Are there different types?

In the wake of the LLM explosion, many folks are understandably playing catch up. In the following, we’ll look at the fundamentals of LLMs and address some common questions. Further, you’ll find some tips on how to use them effectively with the world’s largest cloud provider, Amazon Web Services (AWS).

How do LLMs work?

LLMs are neural networks trained on a mass of data to understand and generate human language. These networks are made up of layers of interconnected neurons, each receiving input signals from others. These are processed through an activation function and then output signals are sent onto the next layer. The connections between nodes have adjustable weights, referred to as parameters. And in the case of LLMs, we’re talking about billions to trillions of parameters, used to identify and map complex patterns like human language.

LLMs learn to match patterns involving words and sentences. The neurons and connections between them are able to create very sophisticated language structures and patterns. The end result is a model so laden with textual data that it can continue sentences, respond to questions, summarize content and more. Basically, it matches the patterns you give it with those it learned during training.

Still, as impressive as LLMs can be, they lack real reasoning and comprehension. They work from probability, not actual knowledge. Yet, with the proper prompts and tuning, they can simulate comprehension so long as the domains and tasks are very focused.

Are There Different Types of LLMs?

Right now, there are primarily three types of LLM models, each with a unique approach. They are:

• Autoregressive: These try to predict the first or final word in a sentence, with context surfacing from previous words. These are best for text generation, though they’re also good for classification and summarization. Autoregressive models are being hailed lately for their ability to generate content.
• Autoencoding: This type of model is trained on text with missing words, enabling it to learn context to then predict information that’s missing. These models beat autoregressive ones when it comes to understanding context, but they don’t generate text reliably. On the upside, autoencoding models are not as compute-intensive and have proven very effective for summarizing and classification.
• Seq2Seq: This text-to-text model combines the approaches of both autoregressive and autoencoding. As a result, they’re particularly effective when it comes to text summarization and translation.

How Do LLMs Work with AWS?

Cloud providers have resources in place to help construct LLMs. As an example, we’ll look at the LLM-related services of AWS, robust offerings enabling the creation of LLM-powered apps without training from scratch.

First up is Amazon Bedrock, a fully managed service that simplifies the creation and scaling of gen AI apps. It provides access to an array of foundation models through a single API. These come from top AI companies like Anthropic, Cohere, Meta, Mistral AI and more. Key features include:

• Choice and Customization: Allows users to choose from a variety of foundation models to find the best fit. They can also be customized with user data, allowing for personalized and domain-specific applications.
• Serverless Experience: A serverless architecture eliminates the need for users to manage infrastructure. It also enables easy integration and deployment of AI into apps with familiar AWS tools.
• Security and Privacy: Bedrock ensures security and privacy, while strongly adhering to AI principles. Users can also keep their data private and secure working with advanced AI capabilities.
• Knowledge Base: Lets architects enhance AI apps with retrieval augmented generation (RAG) techniques. It aggregates various data sources into a central repository so models and agents are current while returning context-specific and accurate responses. It supports seamless integration with widely used storage databases, so RAG can be implemented without managing complex, underlying infrastructure.
• Agents: Architects can build and configure autonomous agents within apps. This makes it easier for end-users to complete actions with data and user input. Agents orchestrate model interactions, data sources, software apps and conversations. They also automatically call APIs and leverage knowledge bases to greatly reduce development efforts.

Next is Amazon SageMaker JumpStart, a machine-learning hub offering pre-trained models and solution templates for various problem scenarios. It allows for incremental training and makes it easy to deploy, fine-tune and try popular models in the infrastructure of your choice. These ready-made solutions save a lot of development time.

Finally, there’s Amazon Q. This GenAI-powered assistant focuses on business needs and allows users to make customizations for specific apps. It’s gained a reputation as a versatile tool, facilitating the creation, operation and understanding of apps and workloads.

Knowledge is Key

Understanding LLMs, how they work and what they are capable of is critical for successful project outcomes. Not only that, teams must be aware of the available tools and which are best suited to work with their data sets. If you lack the in-house expertise, consult with a knowledgeable partner. Working with LLMs requires enormous resources and compute costs can quickly get out of hand without moving the project closer to success. But a little understanding will go a long way to getting across the finish line without breaking the bank.