課程資料
時間表
- 2026年9月22日(週二) - 2026年9月23日(週三) 上午 9:30 - 下午 5:00
- 2026年10月22日(週四) - 2026年10月23日(週五) 上午 9:30 - 下午 5:00
- 2026年11月23日(週一) - 2026年11月24日(週二) 上午 9:30 - 下午 5:00
- 2026年12月24日(週四) - 2026年12月25日(週五) 上午 9:30 - 下午 5:00
報名日期
2026年6月22日(週一) - 2026年12月23日(週三)
價錢
HKD 9,600
(Early Bird HK9600
Standard HK12000)
(Early Bird HK9600
Standard HK12000)
課程級別
學習模式
修業期
2 日
教學語言
廣東話
地點
2/F, Centre Point, 181 Gloucester Road, Wanchai, HK
證書
- 可獲發
- *證書的發放與分配，依課程提供者的政策及安排而定。
4次瀏覽
課程簡介
Course description
In this advanced two-day course, software developers learn to build and customize AI solutions by using Amazon
Bedrock programmatically. Through hands-on exercises and labs, participants will invoke foundation models through
Amazon Bedrock APIs, implement Retrieval Augmented Generation (RAG) patterns with Amazon Bedrock Knowledge
Bases, and develop AI agents with tool integration. The course focuses on the practical implementation of prompt
engineering techniques, responsible AI practices with Amazon Bedrock Guardrails, open source framework
integration, and architectural patterns for real-world business applications.
- • Course level: Advanced
- • Duration: 2 days
Activities
- This course includes presentations, demonstrations, hands-on labs, and group exercises.
Course objectives
In this course, you will:
- • Develop generative AI applications using Amazon Bedrock.
- • Design architecture patterns of generative AI applications.
- • Configure Amazon Bedrock APIs to invoke foundation models (FMs) programmatically. Develop
- agentic AI applications by integrating Amazon Bedrock tools and open source frameworks.
- • Build custom solutions with Retrieval Augmented Generation (RAG) and Amazon Bedrock Knowledge
- Bases.
- • Integrate open source SDKs with Amazon Bedrock to build business.
- • Optimize model responses by applying prompt engineering techniques.
- • Evaluate generative AI application components.
- • Implement responsible AI practices to protect generative AI
課程內容
Day 1
Module 1: Exploring Components of Generative AI Applications on AWS
- • Understanding generative AI concepts
- • Identifying AWS generative AI stack components
- • Designing generative AI application components
Module 2: Programming with Amazon Bedrock
- • Guiding model response generation
- • Using Amazon Bedrock programmatically Hands-on lab: Develop with Amazon Bedrock APIs
- • Hands-on lab: Develop Streaming Patterns with Amazon Bedrock APIs
Module 3: Applying Prompt Engineering for Developers
- • Introducing prompt engineering
- • Introducing prompt techniques
- • Optimizing prompts for better results
Module 4: Using Amazon Bedrock APIs in Common Architectures
- • Implementing architecture patterns with Amazon Bedrock APIs
- • Exploring common use cases
- • Adding conversational memory to extend context
- • Hands-on lab: Develop Conversation Patterns with Amazon Bedrock APIs
Module 5: Customizing Generative AI Responses with RAG
- • Implementing Retrieval Augmented Generation (RAG)
- • Using Amazon Bedrock Knowledge Bases
- • Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock
- Knowledge Bases
Module 6: Integrating Open Source Frameworks with Amazon Bedrock
- • Invoking a foundation model in Amazon Bedrock using LangChain
- • Using LangChain for context-aware responses
- • Hands-on lab: Develop a Generative AI Application Pattern using Open Source Frameworks and
- Amazon Bedrock Knowledge Bases
Day 2
Module 7: Evaluating Generative AI Application Components
- • Evaluating application components
- • Evaluating model output
- • Evaluating RAG output
- • Optimizing latency and cost
- • Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications
Module 8: Implementing Responsible AI
- • Understanding responsible AI
- • Mitigating bias and addressing prompt misuses
- • Using Amazon Bedrock Guardrails
- • Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails
Module 9: Using Tools and Agents in Generative AI Applications
- • Using tools
- • Understanding AI agents
- • Understanding open source agentic frameworks
- • Understanding agent interoperability
Module 10: Developing Amazon Bedrock Agents
- • Implementing Amazon Bedrock Flows
- • Designing Amazon Bedrock Agents
- • Developing Amazon Bedrock Inline Agents
- • Designing multi-agent collaboration
- • Using Amazon Bedrock AgentCore
- • Hands-on lab: Developing Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge
- Bases and Guardrails
- • Course Wrap-Up
