- Module 1: Get started with language models in 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.
- Module 2: Implement Retrieval Augmented Generation (RAG) with Azure Databricks
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.
- Module 3: Implement multi-stage reasoning in Azure Databricks
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.
Implement Generative AI engineering with Azure Databricks
Overview
Syllabus
- Module 1: Module 1: Get started with language models in Azure Databricks
- 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
- Module 2: Module 2: Implement Retrieval Augmented Generation (RAG) with Azure Databricks
- 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
- Module 3: Module 3: Implement multi-stage reasoning in Azure Databricks
- 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
