Overview of Large Language Models

Large Language Models (LLMs) represent a significant advancement in artificial intelligence, transforming how we interact with technology and manage complex tasks. When I joined Samsung in 2019 to do AI, LLMs like today’s version of ChatGPT were still science fiction. Fast forward to now and I can’t imagine writing or coding without one.

At a high level, LLMs are sophisticated AI systems trained on vast amounts of text data to understand, generate, and manipulate human language. Their ability to comprehend and produce text with a high degree of fluency and accuracy makes them powerful tools for a wide range of applications but can be especially beneficial as you’ll see later on in this guide.

What Are LLMs?

LLMs are built using deep learning techniques, specifically leveraging transformer architecture (TODO: NEED TO EXPLAIN WHAT THIS IS).

These models are pre-trained on diverse and extensive datasets, which enables them to understand context, infer meaning, and generate coherent and contextually appropriate text based on given prompts. The level of interaction you can get out of these LLMs when using them for coding never ceases to impress me.

Here are some common types of LLMs:

Model Type	Description	Use Cases
GPT (Generative Pre-trained Transformer)	Developed by OpenAI, GPT models are designed to generate human-like text based on input prompts. They are known for their versatility and ability to handle a wide range of natural language processing tasks.	Content creation, conversational agents, text completion, and more.
BERT (Bidirectional Encoder Representations from Transformers)	Developed by Google, BERT models are designed to understand the context of words in a sentence by looking at both directions (left and right). This bidirectional approach helps in tasks that require understanding context, such as question answering and sentiment analysis.	Search engines, sentiment analysis, question answering systems, and more.
LLaMA (Large Language Model Meta AI)	Developed by Meta, LLaMA models are designed to be more efficient and accessible, providing high performance on language tasks with relatively fewer computational resources. They are suitable for both research and practical applications.	Research in language modeling, practical applications in resource-constrained environments.

How Developers Can Use LLMs

Developers can leverage LLMs in numerous ways to enhance their applications and workflows. One of the primary uses is in natural language processing (NLP) tasks, where LLMs can help with understanding and generating human language. This includes applications like chatbots, virtual assistants, and automated customer support, where LLMs can provide more natural and accurate interactions. All of this can be leveraged by developers for more than content creation. LLMs can generate new code, find errors in existing code, and even add detailed contextual comments. When used correctly, they can save you a lot of time and effort, so you can focus on the bigger picture while the LLM handles repetitive or time-consuming tasks.

LLMs are also powerful tools for data analysis and manipulation. They can help extract insights from large datasets, summarize information, and automate the creation of reports. For instance, developers can use LLMs to help clean up data, generate dummy data for testing and create instructional guides. Moreover, LLMs can assist in coding by providing auto-completion, code generation, and debugging assistance. This enhances the productivity of developers by reducing the time spent on routine coding tasks and allowing them to focus on more complex problems. If you don’t remember what something in your code does or want to understand code that someone else has written, just ask the LLM.

In research and development (R&D), LLMs offer a valuable ability to help you experiment with new ideas and pushing the boundaries of what you are capable of which is an incredibly powerful tool when you are learning a new language or trying to follow development best practices. Developers can fine-tune these models for specific applications, improving their performance on niche tasks and expanding their utility. Hate writing unit tests? Let the LLM do it for you. You’d be surprised at how they can help you optimize and refactor your code based on your direction or its own knowledge.

By integrating LLMs into your workflow, You can harness the power of advanced LLMs to build smarter, more efficient, and more responsive code. This not only improves the end-user experience but also accelerates development cycles when leveraged correctly. But don’t take my word for it, you should give it a try. Most of this guide was generated by an LLM and I cleaned it up to add my own perspective and verify what I’m learning is accurate since this is my first time focusing on running these models locally.