This repo contains code examples, demos, educational and training content, notebooks, and other technical materials built by the Google Cloud Generative AI Solutions and Business team.
- Tuning Foundational Models with Vertex AI: A comprehensive Jupyter notebook illustrating the step-by-step procedure for tuning foundational models (PaLM 2) with Google Cloud's Vertex AI. Guides users through the entire setup and integration process – starting from environment setup, foundational model selection, to tuning it with Vertex AI.
- Langchain Observability Code Snippet: A Langchain callback to aid with understanding/observing the exact LLM calls made by a Langchain agent. The callback is provided in a Jupyter notebook, which also includes a demonstration of the code snippet.
- Advanced Prompting Training: A detailed notebook on prompt engineering, demonstrating and explaining chain of thought and ReAct (reasoning + acting) prompting. Chain of thought is a very low-effort way to improve prompt performance, and ReAct is the state-of-the-art for using LLMs to interact with external systems.
If you have any questions or if you found any problems with this repository, please report through GitHub issues.