This two-day 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.
This course is designed to teach participants how to:
Describe generative AI and how it aligns to machine learning.
Define the importance of generative AI and explain its potential risks and benefits.
Identify business value from generative AI use cases.
Discuss the technical foundations and key terminology for generative AI.
Explain the steps for planning a generative AI project.
Identify some of the risks and mitigations when using generative AI.
Understand how Amazon Bedrock works.
Familiarise yourself with basic concepts of Amazon Bedrock.
Recognise the benefits of Amazon Bedrock.
List typical use cases for Amazon Bedrock.
Describe the typical architecture associated with an Amazon Bedrock solution.
Understand the cost structure of Amazon Bedrock.
Implement a demonstration of Amazon Bedrock in the AWS Management Console.
Define prompt engineering and apply general best practices when interacting with FMs.
Identify the basic types of prompt techniques, including zero-shot and few-shot learning.
Apply advanced prompt techniques when necessary for your use case.
Identify which prompt-techniques are best-suited for specific models.
Identify potential prompt misuses.
Analyse potential bias in FM responses and design prompts that mitigate that bias.
Identify the components of a generative AI application and how to customise a foundation model (FM).
Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs.
Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications.
Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock.
Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications.
Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach.
Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.
This course is intended for:
Software developers interested in leveraging large language models without fine-tuning
We recommend that attendees of this course have:
Completed AWS Technical Essentials
Intermediate-level proficiency in Python
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
