Tekst genereren met taalmodellen

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Deze oefening maakt deel uit van de cursus

Introductie tot Amazon Bedrock

SkillTag.level.intermediateSkillTag.label

4.8+

Learn the fundamentals of Amazon Bedrock, AWS's fully managed foundation model service. Start with basic setup and authentication, explore available foundation models like Claude and Titan, and master essential API interactions. This chapter covers setting up a development environment, choosing appropriate models for a use case, and implementing proper API request handling, including streaming responses and error management.

Exercise 1: Accessing Amazon Bedrock Exercise 2: Initializing the Bedrock client Exercise 3: Retrieving Bedrock model details Exercise 4: Selecting a foundation model Exercise 5: Using the correct model in Bedrock Exercise 6: Generating text in Bedrock

In this chapter, you'll progress through three key lessons. Starting with the basics of text generation and response handling, you'll then advance to sophisticated prompt engineering techniques like few-shot learning and structured outputs. The final lesson covers model parameter optimization, teaching learners to fine-tune settings like temperature and token limits for optimal results. Each lesson builds upon the previous, moving from fundamental API interactions to advanced parameter control for specific use cases.

Exercise 1: Tekst genereren met taalmodellen

Huidige oefening

Exercise 2: Prompts verbeteren met roltoewijzing Exercise 3: Modelreacties verwerken en foutafhandeling Exercise 4: Categorisatie van foundation model-rapport Exercise 5: Prompt engineering-technieken toepassen Exercise 6: Implementeer few-shot learning voor meetkunde Exercise 7: Gestructureerde output-prompts maken Exercise 8: Werken met verschillende modelparameters Exercise 9: Creativiteit aanpassen met temperatuurregeling Exercise 10: Uitvoer sturen met top_p en max_tokens Exercise 11: Parameters voor productbeschrijvingen

In this chapter, you’ll build robust and responsible applications with Amazon Bedrock, moving beyond basic API interactions. Starting with conversational AI, you’ll master state management and context handling to create coherent chat experiences. You’ll then explore advanced prompting techniques, learning how to generate diverse content types while maintaining consistent style and tone. The chapter covers critical production aspects like error handling, rate limiting, and efficient response processing. Finally, you will implement ethical guardrails and safety measures, ensuring your AI applications are both powerful and responsible.

Exercise 1: Ethical considerations and guardrails Exercise 2: Product review moderation Exercise 3: Creating age-appropriate content filters Exercise 4: Managing AI conversations Exercise 5: Building a conversation-manager class Exercise 6: Making a conversation memory-aware Exercise 7: Advanced prompting strategies Exercise 8: Implementing chain of thought prompting Exercise 9: Styled content generation with Bedrock Exercise 10: Handling API responses and errors Exercise 11: Implementing rate limit handling Exercise 12: Implementing model fallbacks Exercise 13: Congratulations!