Course Objectives

• Introduction
• Models, prompts, and parsers
• Chains
• Questions and answers
• Agents
• Workshop
• Document loading
• Document splitting
• Vectorstores and embeddings
• Retrieval
• Question answering with documents
• Chat with documents
• Workshop

Course Content

Introduction to the Course

In an era where artificial intelligence (AI) is not just an asset but a necessity, the advent of Large Language Models (LLMs) such as ChatGPT https://chat.openai.com/, Claude https://claude.ai/login?returnTo=%2F, and Bard https://bard.google.com/chat has marked a revolutionary shift in how we interact with digital technologies. These models have demonstrated unparalleled capabilities in understanding, generating, and processing natural language, opening new frontiers in AI application development. The “LLM Use: Application and Proprietary Data” course is designed to position professionals at the forefront of this technological revolution. Over two days, this course offers a deep dive into the practical application of LLMs, focusing on leveraging proprietary data for multistep reasoning—a critical skill in today’s data-driven landscape.

The Need for Specialized Knowledge in LLMs

The integration of LLMs into business processes and applications promises not just incremental improvements but a quantum leap in productivity and innovation. However, harnessing these models’ full potential requires a nuanced understanding of their capabilities, limitations, and the best practices for their application. This course addresses a significant gap in the market: the need for comprehensive, hands-on training that empowers professionals to build LLM-based applications that can intelligently process and analyze proprietary data.

Who Should Attend?

Targeted at developers, data scientists, team leads, and project managers, the course caters to a wide range of professionals involved in or overseeing AI projects. It is particularly beneficial for those in intermediate to advanced stages of their career who possess a foundational understanding of machine learning and seek to specialize in the cutting-edge domain of LLMs.

Course Structure and Content

The course is meticulously structured to balance theoretical knowledge with practical application. The curriculum is divided into several key modules, each designed to build upon the previous one, ensuring a coherent and comprehensive learning journey.

• Introduction: Sets the stage for the course, providing an overview of LLMs, their significance, and their potential to transform various industries.
• Models, Prompts, and Parsers: Delves into the technical underpinnings of LLMs, offering insights into how these models are constructed, how they process language, and how they can be effectively prompted for specific tasks.
• Chains, Questions and Answers, Agents: Explores advanced concepts in LLM application, including the construction of chains of thought for complex reasoning, the development of Q&A systems, and the creation of AI agents that can perform a variety of tasks.
• Document Handling Techniques: Focuses on the practical aspects of integrating proprietary data with LLMs, covering document loading, splitting, and the use of vectorstores and embeddings for efficient data retrieval.
• Practical Workshops: Two hands-on workshops provide participants with the opportunity to apply what they have learned, tackling real-world problems and developing solutions using LLMs.

Hands-On Learning Experience

A distinctive feature of this course is its emphasis on practical, hands-on learning. Participants will engage in labs and workshops that simulate real-world scenarios, allowing them to apply theoretical knowledge to practical challenges. The course’s lab environment, hosted in the cloud, requires no software installation, ensuring that participants can focus on learning without technical hindrances.

The Impact of the Course on Your Career

Upon completion of this course, participants will have gained not only a deep understanding of LLMs and their application but also practical experience in integrating these models with proprietary data. This unique skill set is highly sought after in the job market, positioning course graduates as leading candidates for roles in AI development and project management. The certification awarded at the end of the course serves as a testament to the participant’s expertise, enhancing their professional profile and opening doors to new career opportunities.

Why This Course Is Essential Now

The pace at which LLMs are evolving and being adopted across industries means that the demand for skilled professionals who can navigate these technologies is growing exponentially. This course offers a timely opportunity for professionals to upskill and align themselves with the future of technology. Whether you aim to lead AI projects, develop innovative applications, or drive strategic decisions in your organization, the knowledge and skills acquired through this course will be invaluable.

Conclusion

The “LLM Use: Application and Proprietary Data” course represents a unique convergence of theoretical depth and practical application in the field of Large Language Models. It is an essential stepping stone for professionals aiming to leverage the power of AI in their careers. By focusing on the integration of proprietary data with LLMs, the course addresses one of the most critical challenges and opportunities in AI application development today. As we stand on the brink of a new era in technology, the skills and knowledge imparted by this course will not only empower individuals but also shape the future of industries worldwide.

See also our Prompt Engineering Course: https://skilldevelopers.com/event/prompt-engineering-fundamentals/

Who should attend

• Developers and data scientists looking to integrate LLMs into their projects.
• Team leads and project managers seeking to oversee LLM-based application development.
• Professionals with a general familiarity with machine learning, looking to specialize in the cutting-edge field of LLMs.

Prerequisites

• A general familiarity with machine learning concepts.
• A reasonably modern laptop with an unrestricted Internet connection. A checklist to verify connectivity will be provided.