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.
Objetivos
En este curso, los participantes aprenderán las siguientes habilidades:
- 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.
Público objetivo
Público objetivo
Esta clase está dirigida al siguiente público:
- Application developers and others who wish to leverage LLMs in applications.
Requisitos previos
Para aprovechar al máximo este curso, los participantes deben cumplir con los siguientes criterios:
- Completion of Introduction to Developer Efficiency with Gemini on Google Cloud or equivalent knowledge.
Duración
1 día
Inversión
Resumen del curso
- Explore the different options available for using generative AI on Google Cloud.
- Use Vertex AI Studio to test prompts for large language models.
- Understand how Vertex AI Studio keeps your data secure.
- Understand basic concepts and components of LangChain.
- Develop LLM-powered applications using LangChain and LLM models on Vertex AI.
- Apply prompt engineering techniques to improve the output from LLMs.
- Implement a RAG architecture to ground LLM models.
- Understand the concept of memory for mult-iturn chat applications.
- Build a multi-turn chat application by using the PaLM API and LangChain.