Generative AI Application Development
Contact us to book this course
Learning Track
Generative AI
Delivery methods
On-Site, Virtual
Duration
1/2 day
Ready for information and practical experience in building advanced LLM applications using multi-stage reasoning LLM chains and agents? You’re in the right place. First, you’ll learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, we’ll show you how to construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, you’ll be introduced to agents and will design an autonomous agent using generative models on Databricks.
Objectives
- Explain how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases.
- Construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers.
- Design an autonomous agent using generative models on Databricks.
Prerequisites
- Familiarity with natural language processing concepts
- Familiarity with prompt engineering/prompt engineering best practices
- Familiarity with the Databricks Data Intelligence Platform
- Familiarity with RAG (preparing data, building a RAG architecture, concepts like embedding, vectors, vector databases, etc.)
Course outline
- Defining Compound AI Systems
- Designing Compound AI Systems
- Deconstructing and Planning a Use Case
- Planning an AI System for Product Quality Complaints
- Introduction to Multi-Stage Reasoning Chains
- Databricks Products for Building Multi-Stage Reasoning Systems
- Building Multi-Stage Reasoning Chains in Databricks
- Introduction to Agents
- Tools for Building Agents
- Multi-Modal AI
- Agent Design in Databricks
- Creating a ReAct Agent
