Hands-On AI: Building Your First LLM-Powered App

Course details

Are you ready to start building applications with large language models (LLMs), but not sure where to begin? This course, which is designed uniquely for beginners with no experience in the LLM space, offers an overview of the fundamentals of LLMs with hands-on challenges to boost your skills along the way. Explore the essentials of retrieval-augmented generation including search engine basics, embedding model limitations, and how to build a chat-with-PDF application. Along the way, instructor Han Lee shows you prompt engineering techniques, including strategies for context engineering to effectively manage and optimize the input provided to LLMs and monitor their insights through observability tools like LangSmith.

Skills covered

Machine Learning FundamentalsTraditional AI and Machine LearningTelecommunicationsAI Development Tools and PlatformsProgramming FoundationsBuilding with AIFull-Stack Web DevelopmentPythonProjectArtificial Intelligence (AI)Web DevelopmentNetwork and System AdministrationOpen SourceSoftware Development

Concepts

Introduction

Building apps using large language models (LLMs)

LLM - The Essentials

Language models and tokenization
LLM capabilities
Challenge - Introduction to Streamlit
Solution - Introduction to Streamlit solution
Prompts and prompt templates
Obtaining an OpenAI token
Challenge - Adding an LLM to the Streamlit app
Solution - Adding an LLM to the Streamlit app
Limitations of LLMs

Retrieval-Augmented Generation (RAG)

Introducing RAG
Search engine basics
Embedding search
Embedding model limitations
Challenge - Enabling PDF uploads in the Streamlit app
Solution - Enabling PDF uploads in the Streamlit app
Challenge - Indexing documents into a vector database
Solution - Indexing documents into a vector database
Challenge - Putting it all together
Solution - Putting it all together

Prompt Engineering

Prompt engineering basics
Challenge - Set up prompting and LangSmith
Solution - Set up prompting and LangSmith
Challenge - Deploying your app on Hugging Face
Solution - Deploying your app on Hugging Face

Conclusion

Continue your LLM journey