Overview
This two-day advanced course is designed for software developers seeking to leverage large language models (LLMs) without fine-tuning, using Amazon Bedrock and LangChain. It covers the basics of generative AI, the foundations of prompt engineering, and architecture patterns for building generative AI applications.
Prerequisites
- Completion of AWS Technical Essentials
- Intermediate proficiency in Python
Target Audience
This course is intended for software developers who:
- Want to integrate generative AI models into their applications
- Are interested in Amazon Bedrock and LangChain for generative AI use cases
Delegates will learn how to
By the end of this course, you will be able to:
- Describe generative AI and its alignment with machine learning
- Identify the business value of generative AI use cases
- Plan and mitigate risks in generative AI projects
- Understand and implement Amazon Bedrock for generative AI applications
- Apply prompt engineering techniques
- Build and secure generative AI applications using Amazon Bedrock and LangChain
- Design and implement architecture patterns for various generative AI use cases
Outline
Day One
Module 1: Introduction to Generative AI - Art of the Possible
- Overview of machine learning
- Generative AI use cases
- Risks and benefits of generative AI
Module 2: Planning a Generative AI Project
- Steps in planning
- Identifying risks and mitigation strategies
Module 3: Getting Started with Amazon Bedrock
- Introduction to Amazon Bedrock
- Setting up and using Bedrock in the AWS Console
- Hands-on demonstration
Module 4: Foundations of Prompt Engineering
- Basics of prompt engineering
- Advanced techniques and addressing prompt misuse
- Mitigating bias in prompts
- Hands-on demonstration: Prompt fine-tuning and bias mitigation
Day Two
Module 5: Amazon Bedrock Application Components
- Overview of application components (e.g., datasets, embeddings)
- Introduction to RAG (Retrieval Augmented Generation)
- Securing applications
Module 6: Amazon Bedrock Foundation Models
- Amazon Bedrock models and methods
- Hands-on lab: Zero-shot text generation
Module 7: LangChain
- Integrating AWS with LangChain
- Using LangChain agents for prompt templates, chat models, and document loaders
- Hands-on lab: Building applications with LangChain
Module 8: Architecture Patterns
- Generative AI architecture patterns
- Hands-on labs: Text summarisation, chatbots, question answering, and code generation using Amazon Bedrock and LangChain
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