Training: Developing Generative AI Applications on AWS

This course is designed to introduce generative artificial intelligence (AI) to software developers interested in using large language models (LLMs) 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.

In this course, you will learn to:

• Understand generative AI and its relation to machine learning
• Identify the risks, benefits, and business value of generative AI
• Plan and execute a generative AI project, including risk mitigation
• Use Amazon Bedrock, its use cases, and cost structure
• Apply prompt engineering best practices with foundation models (FMs)
• Utilize basic and advanced prompt techniques, including zero-shot and few-shot learning
• Address potential biases and misuses in prompts
• Understand generative AI application components and customize FMs
• Use LangChain with LLMs for building AI applications Implement architecture patterns for generative AI solutions, such as text summarization, question answering, * and chatbots
• Integrate LangChain with Bedrock for enhanced AI model perform

We recommend that attendees of this course have:

• Completed AWS Technical Essentials
• Intermediate-level proficiency in Python

2 days / € 1,500.00 (excl. tax) per person (DE)

• Overview of ML
• Basics of generative AI
• Generative AI use cases
• Generative AI in practice
• Risks and benefits

• Generative AI fundamentals
• Generative AI in practice
• Generative AI context
• Steps in planning a generative AI project
• Risks and mitigation

• Introduction to Amazon Bedrock
• Architecture and use cases
• How to use Amazon Bedrock
• Demonstration: Setting up Bedrock access and using playgrounds

• Basics of foundation models
• Fundamentals of prompt engineering
• Basic prompt techniques
• Advanced prompt techniques
• Model-specific prompt techniques
• Demonstration: Fine-tuning a basic text prompt
• Addressing prompt misuses
• Mitigating bias
• Demonstration: Image bias mitigation

• Overview of generative AI application components
• Foundation models and the FM interface
• Working with datasets and embeddings
• Demonstration: Word embeddings
• Additional application components
• Retrieval Augmented Generation (RAG)
• Model fine-tuning
• Securing generative AI applications
• Generative AI application architecture

• Introduction to Amazon Bedrock foundation models
• Using Amazon Bedrock FMs for inference
• Amazon Bedrock methods
• Data protection and auditability
• Demonstration: Invoke Bedrock model for text generation using zero-shot prompt

• Optimizing LLM performance
• Using models with LangChain
• Constructing prompts
• Demonstration: Bedrock with LangChain using a prompt that includes context
• Structuring documents with indexes
• Storing and retrieving data with memory
• Using chains to sequence components
• Managing external resources with LangChain agents

• Introduction to architecture patterns
• Text summarization
• Demonstration: Text summarization of small files with Anthropic Claude
• Demonstration: Abstractive text summarization with Amazon Titan using LangChain
• Question answering
• Demonstration: Using Amazon Bedrock for question answering
• Chatbot
• Demonstration: Conversational interface – Chatbot with AI21 LLM
• Code generation
• Demonstration: Using Amazon Bedrock models for code generation
• LangChain and agents for Amazon Bedrock
• Demonstration: Integrating Amazon Bedrock models with LangChain agents

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

The practical exercises are performed in prepared working environments available via web browser – no software needs to be installed. The course material is in English, spoken language can be in german or english. Other languages like spanish, portuguese or french, please contact us under training@tecracer.de