Implement Generative AI engineering with Azure Databricks

In this module, you learn how to:

• Describe Generative AI.
• Describe Large Language Models (LLMs).
• Identify key components of LLM applications.
• Use LLMs for Natural Language Processing (NLP) tasks.

In this module, you learn how to:

• Set up a RAG workflow.
• Prepare your data for RAG.
• Retrieve relevant documents with vector search.
• Improve model accuracy by reranking your search results.

In this module, you learn how to:

• Identify the need for multi-stage reasoning systems.
• Describe a multi-stage reasoning workflow.
• Implement multi-stage reasoning with libraries like LangChain, LlamaIndex, Haystack, and the DSPy framework.

• Introduction
• Understand Generative AI
• Understand Large Language Models (LLMs)
• Identify key components of LLM applications
• Use LLMs for Natural Language Processing (NLP) tasks
• Exercise - Explore language models
• Knowledge check
• Summary

• Introduction
• Explore the main concepts of a RAG workflow
• Prepare your data for RAG
• Find relevant data with vector search
• Rerank your retrieved results
• Exercise - Set up RAG
• Knowledge check
• Summary

• Introduction
• What are multi-stage reasoning systems?
• Explore LangChain
• Explore LlamaIndex
• Explore Haystack
• Explore the DSPy framework
• Exercise - Implement multi-stage reasoning with LangChain
• Knowledge check
• Summary