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.

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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
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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
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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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Why choose QA

Special Notices

Labs - Please note: The labs for your AWS course will be delivered through AWS Builder labs. In order to access these labs you will need to have an Amazon BuilderID. You can set up your new Amazon account here. Please ensure that you have set up this Amazon BuilderID in advance of attending your class.

Courseware – Please note: In order to access your digital course materials you are required to set up a Gilmore account in advance of attending your course. To do this please follow this link.

Please also be aware that in order to access your materials and Labs it is important that your device and network should not restrict access to AWS or Vitalsource content. For that reason, AWS recommend NOT using a Corporate laptop with any security restrictions in place or the use of a VPN.

Dates & Locations

Need to know

Frequently asked questions

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All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.

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When will I receive my certificate?

Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.

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