Hands-On AI: Building LLM-Powered Apps
1h 16mIntermediate2024-02-26
Authors
Han-chung Lee
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 how to get up and running with prompt engineering, using the prompt playground for LLM apps.
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 how to get up and running with prompt engineering, using the prompt playground for LLM apps.
Skills covered
Mobile Device ManagementNatural Language Processing (NLP)Full-Stack Web DevelopmentPythonProjectArtificial Intelligence (AI)Web DevelopmentNetwork and System AdministrationOpen Source
Concepts
0. Introduction
- 01 - Building apps using large language models
1. LLM - The Essentials
- 02 - Language models and tokenization
- 03 - Large language model capabilities
- 04 - Challenge - Introduction to Chainlit
- 05 - Solution - Introduction to Chainlit solution
- 06 - Prompts and prompt templates
- 07 - Obtaining an OpenAI token
- 08 - Challenge - Adding an LLM to the Chainlit app
- 09 - Solution - Adding an LLM to the Chainlit app
- 10 - Large language model limitations
2. Retrieval Augmented Generation
- 11 - Retrieval augmented generation
- 12 - Search engine basics
- 13 - Embedding search
- 14 - Embedding model limitations
- 15 - Challenge - Enabling load PDF to Chainlit app
- 16 - Solution - Enabling load PDF to Chainlit app
- 17 - Challenge - Indexing documents into a vector database
- 18 - Solution - Indexing documents into a vector database
- 19 - Challenge - Putting it all together
- 20 - Solution - Putting it all together
- 21 - Trying out your chat with the PDF app
3. Prompt Engineering
- 22 - Prompt engineering basics
- 23 - Challenge - Set up prompting
- 24 - Solution - Set up prompting
- 25 - Prompt playground for LLM apps
- 26 - Challenge - Fixing hallucination via prompting
- 27 - Solution - Fixing hallucination via prompting
Conclusion
- 28 - Continue your LLM journey