Course Overview
In this course, you explore tools and APIs available on Google Cloud for integrating large language models (LLMs) into your application. After exploring generative AI options on Google Cloud, next you explore LLMs and prompt design in Vertex AI Studio. Then you learn about LangChain, an open-source framework for developing applications powered by language models. After a discussion around more advanced prompt engineering techniques, you put it all together to build a multi-turn chat application by using LangChain and the Vertex AI PaLM API.
Who should attend
Application developers and others who wish to leverage LLMs in applications.
Prerequisites
Completion of Introduction to Developer Efficiency on Google Cloud (IDEGC) or equivalent knowledge.
Course Objectives
- Explore the different options available for using generative AI on Google Cloud.
- Use Vertex AI Studio to test prompts for large language models.
- Develop LLM-powered applications using LangChain and LLM models on Vertex AI.
- Apply prompt engineering techniques to improve the output from LLMs.
- Build a multi-turn chat application using the PaLM API and LangChain.
Outline: Application Development with LLMs on Google Cloud (ADLGC)
Module 1 - Introduction to Generative AI on Google Cloud
Topics:
- Vertex AI on Google Cloud
- Generative AI options on Google Cloud
- Introduction to course use case
Objectives:
- Explore the different options available for using generative AI on Google Cloud.
Module 2 - Vertex AI Studio
Topics:
- Introduction to Vertex AI Studio
- Available models and use cases
- Designing and testing prompts in the Google Cloud console
- Data governance in Vertex AI Studio
Objectives:
- Use Vertex AI Studio to test prompts for large language models.
- Understand how Vertex AI Studio keeps your data secure
Activities:
- Lab: Exploring Vertex AI Studio
Module 3 - LangChain Fundamentals
Topics:
- Introduction to LangChain
- LangChain concepts and components
- Integrating the Vertex AI PaLM APIs
- Question/Answering Chain using PaLM API
Objectives:
- Understand basic concepts and components of LangChain
- Develop LLM-powered applications using LangChain and LLM models on Vertex AI
Activities:
- Lab: Getting Started with LangChain + Vertex AI PaLM API
Module 4 - Prompt Engineering
Topics:
- Review of few-shot prompting
- Chain-of-thought prompting
- Retrieval augmented generation (RAG)
- ReAct
Objectives:
- Apply prompt engineering techniques to improve the output from LLMs.
- Implement a RAG architecture to ground LLM models.
Activities:
- Lab: Prompt Engineering Techniques
Module 5 - Creating Custom Chat Applications with Vertex AI PaLM API
Topics:
- LangChain for chatbots
- Memory for multi-turn chat
- Chat retrieval
Objectives:
- Understand the concept of memory for mult-iturn chat applications.
- Build a multi-turn chat application by using the PaLM API and LangChain.
Activities:
- Lab: Implementing RAG Using LangChain