Developing Generative AI Applications on AWS
This course is designed to introduce generative AI to software developers interested in leveraging large language models without fine-tuning. The course provides an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and LangChain.
This course includes presentations, demonstrations, and group exercises.
Target audience and prerequisites
This course is intended for software developers interested in leveraging large language models without fine-tuning.
We recommend that attendees of this course have foundational knowledge of the AWS Platform and intermediate-level proficiency in Python.
Detaljerad information
Module 1: Introduction to Generative AI - Art of the Possible
- Overview of ML
- Basics of generative AI
- Generative AI use cases
- Generative AI in practice
- Risks and benefits
Module 2: Planning a Generative AI Project
- Generative AI fundamentals
- Generative AI in practice
- Generative AI context
- Steps in planning a generative AI project
- Risks and mitigation
Module 3: Getting Started with Amazon Bedrock
- Introduction to Amazon Bedrock
- Architecture and use cases
- How to use Amazon Bedrock
- Demonstration: Setting Up Amazon Bedrock Access and Using Playgrounds
Module 4: Foundations of Prompt Engineering
- Basics of foundation models
- Fundamentals of prompt engineering
- Basic prompt techniques
- Advanced prompt techniques
- Demonstration: Fine-Tuning a Basic Text Prompt
- Model-specific prompt techniques
- Addressing prompt misuses
- Mitigating bias
- Demonstration: Image Bias-Mitigation
Module 5: Amazon Bedrock Application Components
- Applications and use cases
- Overview of generative AI application components
- Foundation models and the FM interface
- Working with datasets and embeddings
- Demonstration: Word Embeddings
- Additional application components
- RAG
- Model fine-tuning
- Securing generative AI applications
- Generative AI application architecture
Module 6: Amazon Bedrock Foundation Models
- Introduction to Amazon Bedrock foundation models
- Using Amazon Bedrock FMs for inference
- Amazon Bedrock methods
- Data protection and auditability
- Lab: Invoke Amazon Bedrock model for text generation using zero-shot prompt
Module 7: LangChain
- Optimizing LLM performance
- Integrating AWS and LangChain
- Using models with LangChain
- Constructing prompts
- Structuring documents with indexes
- Storing and retrieving data with memory
- Using chains to sequence components
- Managing external resources with LangChain agents
Module 8: Architecture Patterns
- Introduction to architecture patterns
- Text summarization
- Lab: Using Amazon Titan Text Premier to summarize text of small files
- Lab: Summarize long texts with Amazon Titan
- Question answering
- Lab: Using Amazon Bedrock for question answering
- Chatbots
- Lab: Build a chatbot
- Code generation
- Lab: Using Amazon Bedrock Models for Code Generation
- LangChain and agents for Amazon Bedrock
- Lab: Building conversational applications with the Converse API
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