Tekst genereren in Bedrock

Na je succes met Claude wil het specialtycoffeebedrijf nu AI-ondersteund schrijven uitrollen naar de marketingafdeling. Ze lanceren een nieuwjaarscampagne en hebben overtuigende onderwerpregels nodig: tijd om met Bedrock een paar pakkende opties te genereren!

De libraries boto3 en json zijn al geïmporteerd. De prompt is al gegeven als: "Write an engaging email subject line for a coffee company's New Year marketing campaign".

Deze oefening maakt deel uit van de cursus

Introductie tot Amazon Bedrock

Cursus bekijken

Oefeninstructies

Stel de sleutels in de dictionary in om de "messages"-lijst voor het Nova-model compleet te maken.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')

# Set the dictionary keys
message = {"____": "user",
           "____": [{"text": prompt}]}

nova_response = bedrock.invoke_model(modelId='amazon.nova-lite-v1:0', body=json.dumps({"messages": [message]}))

print(json.loads(nova_response.get("body").read().decode())["output"]["message"]["content"][0]["text"])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot Amazon Bedrock

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Toegang krijgen tot Amazon Bedrock Exercise 2: De Bedrock-client initialiseren Exercise 3: Bedrock-modeldetails ophalen Exercise 4: Een foundation model kiezen Exercise 5: Het juiste model gebruiken in Bedrock Exercise 6: Tekst genereren in Bedrock

Huidige oefening

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: Generating text with language models Exercise 2: Enhancing prompts with role assignment Exercise 3: Processing model responses and error handling Exercise 4: Foundation model report categorization Exercise 5: Applying prompt engineering techniques Exercise 6: Implement few-shot learning for geometry Exercise 7: Creating structured output prompts Exercise 8: Working with different model parameters Exercise 9: Adjusting creativity with temperature control Exercise 10: Controlling output with top_p and max_tokens Exercise 11: Parameters for product descriptions

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!