Why There’s No Better Time to Learn LLM Development

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

LLMs are already beginning to deliver significant efficiency savings and productivity boosts when assisting workflows for early adopters. However, a large amount of work has to be delivered to access the potential benefits of LLMs and build reliable products on top of these models. This work is not performed by machine learning engineers or software developers; it is performed by LLM developers by combining the elements of both with a new, unique skill set. There are no true “Expert” LLM Developers out there, as these models, capabilities, and techniques have only existed for 2–3 years.

Building LLMs for Production is a resource that addresses this gap by teaching the core principles needed to build production-ready applications with LLMs. It will help you take an early lead in developing the LLM developer skillset and go on to become one of the first experts in this key new field.

To make learning LLM development more accessible, we’ve released an e-book second edition version of Building LLMs for Production on Towards AI Academy at a lower price than on Amazon.

And if you purchased the first edition (prior to October 2024), you’re eligible for an additional discount. Just reach out to [email protected], and we’ll make sure you can upgrade affordably.

What You’ll Find in Building LLMs for Production:

Building LLMs for Production is for anyone who wants to build LLM products that can serve real use cases today. It explores various methods to adapt “foundational” LLMs to specific tasks with enhanced accuracy, reliability, and scalability. It tackles the lack of reliability of “out of the box” LLMs by teaching the AI developer tech stack of the future; Prompting, Fine-Tuning, RAG, and Tools Use.

The core concepts discussed in the book are becoming a foundation for practitioners and companies working with LLMs. The updated version provides more practical information on these techniques, which we believe have become more accessible since the book was published and have found broader applications beyond research.

This book breaks down techniques that are scalable for enterprise-level workflows, helping both independent developers and small companies with limited resources create AI products that deliver value to paying customers.

Additionally, this book comes with access to our webpage where we also share lots of additional up-to-date content, code, notebooks, and resources.

What’s New?

A major addition to the book is a brand-new chapter titled Indexes, Retrievers, and Data Preparation. Indexes, Retrievers, and Data Preparation are the foundational components of a RAG pipeline. The latest version puts more emphasis on these to ensure the RAG pipelines taught in the book help in scaling LLM applications, optimizing performance, and enhancing response quality. Additionally, several updates have been made to the existing chapters to include more examples and a better understanding of real-world applications.

The updated version takes a deeper dive into essential techniques for LLM deployment and optimization, making it more practical and relevant for current AI development needs. With open-source LLMs growing in popularity, this edition also covers the deployment of LLMs on various cloud platforms, including Together AI, Groq, Fireworks AI, and Replicate.

Key Areas of Focus in Building LLMs for Production

1. LLM Fundamentals, Architecture, & LLMs in Practice

• Foundations: Learn the essentials of LLMs, including language modeling, tokenization, embeddings, scaling laws, and the core components of the transformer architecture.
• LLMs in Practice: Explore strategies for handling common challenges like hallucinations and biases, along with different decoding methods and evaluation metrics.

2. Prompting & Frameworks

• Prompting: Master the art of crafting effective prompts, including techniques like zero-shot, few-shot, chain-of-thought, and more.
• Frameworks: Understand how to use LangChain and LlamaIndex to structure LLM applications, from building prompt templates to working with vector stores and data connectors.

3. Retrieval-Augmented Generation (RAG) & Fine-Tuning

• RAG Components: Discover how to use RAG techniques to enhance LLM performance by integrating external data sources, such as PDFs, web pages, and more, with tools like LangChain and LlamaIndex.
• Fine-Tuning Optimization: Dive into advanced fine-tuning methods like LoRA, QLoRA, and supervised fine-tuning.

4. Agents, Optimization, & Deployment

• Agents: Learn how to implement autonomous agents using frameworks like AutoGPT and BabyAGI, and explore simulations and generative agents.
• Optimization & Deployment: Discover best practices for deploying LLMs at scale, including quantization, pruning, and optimizing for CPU/GPU environments.

Get it now on Towards AI Academy at a reduced price, making it easier than ever to add this resource to your toolkit!

A Sneak Peek at Towards AI Academy

When you purchase Building LLMs for Production on Towards AI Academy, you gain access to an entire learning ecosystem. From pre-orders for hands-on AI courses to a variety of generative AI resources, Towards AI Academy offers everything you need to deepen your skills, whether you’re building for your own projects or advancing your career.

Get started with Building LLMs for Production on Towards AI Academy today, and explore more of what we offer as your learning resource for AI and machine learning.

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Published via Towards AI