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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, how to plan a generative AI project, how to get started with Amazon Bedrock, the basics of prompt engineering, and the architectural patterns for building generative AI applications using Amazon Bedrock and LangChain.
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Course Contents
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In diesem Kurs lernen Sie folgendes:
- Beschreibung der generativen KI und wie sie mit maschinellem Lernen zusammenhängt
- Bedeutung generativer KI und ihrer potenziellen Risiken und Vorteile
- Der geschäftliche Nutzen von generativen KI-Anwendungen
- Die technischen Grundlagen und der Schlüsselterminologie für generative KI
- Die Schritte zur Planung eines generativen KI-Projekts
- Identifikation einiger Risiken und Abhilfemaßnahmen beim Einsatz von generativer KI
- Funktionsweise von Amazon Bedrock
- Die grundlegenden Konzepte von Amazon Bedrock
- Die Vorteile von Amazon Bedrock
- Typische Anwendungsfälle für Amazon Bedrock
- Typische Architektur einer Amazon Bedrock-Lösung
- Die Kostenstruktur von Amazon Bedrock
- Demonstration – Implementation von Amazon Bedrock in der AWS Management Console
- Prompt Engineering und Anwendung allgemeiner Best Practices bei der Interaktion mit Foundation Models (FMs)
- Die grundlegenden Arten von Prompt-Techniken, einschließlich Zero-Shot und Little-Shot Learning
- Erweiterte Prompt-Techniken
- Welche Prompt-Techniken für bestimmte Modelle am besten geeignet sind
- Identifizierung von potentiellem Prompt-Missbrauch
- Analyse potenzieller Bias in FM-Antworten und Entwicklung von Prompts, die diesen Bias abschwächen
- Identifizierung der Komponenten einer generativen KI-Anwendung und wie man einen FM anpasst
- Amazon Bedrock Foundation-Modelle, Inferenzparameter und wichtige Amazon Bedrock APIs
- Amazon Web Services (AWS) Services, die bei der Überwachung, Sicherung und Verwaltung Ihrer Amazon Bedrock Anwendungen helfen
- Wie Sie LangChain mit LLMs, Prompt Templates, Chains, Chat-Modellen, Text
Einbettungsmodellen, Document Loaders, Retrievern und Agenten für Amazon Bedrock intergrieren - Architekturmuster, die Sie mit Amazon Bedrock für den Aufbau generativer KI-Anwendungen implementieren können
- Anwendungsbeispiele, die die verschiedenen Amazon Bedrock-Modelle, LangChain und den Retrieval Augmented Generation (RAG) Ansatz verwenden
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Target Group
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- Software developers who want to use LLMs without fine-tuning
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Knowledge Prerequisites
-
We recommend that participants in this course fulfill the following requirements:
- Completed the AWS Technical Essentials course
- Advanced knowledge of Python
| 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 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 |
| Demonstration: Invoke 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 |
| Demonstration: Bedrock with LangChain Using a Prompt that Includes Context |
| Module 8: Architecture Patterns |
| 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 |
| Chatbots |
| 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 Agent |
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Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
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, how to plan a generative AI project, how to get started with Amazon Bedrock, the basics of prompt engineering, and the architectural patterns for building generative AI applications using Amazon Bedrock and LangChain.
-
Course Contents
-
In diesem Kurs lernen Sie folgendes:
- Beschreibung der generativen KI und wie sie mit maschinellem Lernen zusammenhängt
- Bedeutung generativer KI und ihrer potenziellen Risiken und Vorteile
- Der geschäftliche Nutzen von generativen KI-Anwendungen
- Die technischen Grundlagen und der Schlüsselterminologie für generative KI
- Die Schritte zur Planung eines generativen KI-Projekts
- Identifikation einiger Risiken und Abhilfemaßnahmen beim Einsatz von generativer KI
- Funktionsweise von Amazon Bedrock
- Die grundlegenden Konzepte von Amazon Bedrock
- Die Vorteile von Amazon Bedrock
- Typische Anwendungsfälle für Amazon Bedrock
- Typische Architektur einer Amazon Bedrock-Lösung
- Die Kostenstruktur von Amazon Bedrock
- Demonstration – Implementation von Amazon Bedrock in der AWS Management Console
- Prompt Engineering und Anwendung allgemeiner Best Practices bei der Interaktion mit Foundation Models (FMs)
- Die grundlegenden Arten von Prompt-Techniken, einschließlich Zero-Shot und Little-Shot Learning
- Erweiterte Prompt-Techniken
- Welche Prompt-Techniken für bestimmte Modelle am besten geeignet sind
- Identifizierung von potentiellem Prompt-Missbrauch
- Analyse potenzieller Bias in FM-Antworten und Entwicklung von Prompts, die diesen Bias abschwächen
- Identifizierung der Komponenten einer generativen KI-Anwendung und wie man einen FM anpasst
- Amazon Bedrock Foundation-Modelle, Inferenzparameter und wichtige Amazon Bedrock APIs
- Amazon Web Services (AWS) Services, die bei der Überwachung, Sicherung und Verwaltung Ihrer Amazon Bedrock Anwendungen helfen
- Wie Sie LangChain mit LLMs, Prompt Templates, Chains, Chat-Modellen, Text
Einbettungsmodellen, Document Loaders, Retrievern und Agenten für Amazon Bedrock intergrieren - Architekturmuster, die Sie mit Amazon Bedrock für den Aufbau generativer KI-Anwendungen implementieren können
- Anwendungsbeispiele, die die verschiedenen Amazon Bedrock-Modelle, LangChain und den Retrieval Augmented Generation (RAG) Ansatz verwenden
-
Target Group
-
- Software developers who want to use LLMs without fine-tuning
-
Knowledge Prerequisites
-
We recommend that participants in this course fulfill the following requirements:
- Completed the AWS Technical Essentials course
- Advanced knowledge of Python
| 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 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 |
| Demonstration: Invoke 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 |
| Demonstration: Bedrock with LangChain Using a Prompt that Includes Context |
| Module 8: Architecture Patterns |
| 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 |
| Chatbots |
| 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 Agent |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.
Online Training
Trainer language German