Menerapkan prompting rantai pemikiran (chain of thought)

StellarStack akan meluncurkan platform cloud andalannya. Tim pemasaran menginginkan email yang menjelaskan mengapa developer sebaiknya melakukan migrasi. Untuk memandu penalaran Claude, Anda akan menambahkan daftar periksa “think-step” ke prompt.

Pustaka json telah diimpor.

Latihan ini adalah bagian dari kursus

Pengantar Amazon Bedrock

Petunjuk latihan

Uraikan tiga langkah yang harus diambil model saat menyiapkan respons, menggunakan daftar bernomor (1., 2., 3.).
Dengan menggunakan daftar steps untuk chain of thought, isilah bidang "text" pada text_data dengan menambahkan langkah-langkah tersebut setelah "\nSteps: ".

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

def generate_message_content(text_data):
    steps = [
        # Outline the steps
        "____",
        "____",
        "____"
    ]
    # Append the steps to the prompt text
    text_data["text"] += "\nSteps: " + ____
    return text_data

text_data = {"text": "Create a promotional email for the StellarStack cloud platform."}
  
print(generate_message_content(text_data))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Amazon Bedrock

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: 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: 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: Pertimbangan etis dan pagar pembatas Exercise 2: Moderasi ulasan produk Exercise 3: Membuat filter konten sesuai usia Exercise 4: Mengelola Percakapan AI Exercise 5: Membangun kelas pengelola percakapan Exercise 6: Membuat percakapan sadar memori Exercise 7: Strategi prompting tingkat lanjut Exercise 8: Menerapkan prompting rantai pemikiran (chain of thought)

Latihan Saat Ini

Exercise 9: Pembuatan konten bergaya dengan Bedrock Exercise 10: Menangani respons dan error API Exercise 11: Mengimplementasikan penanganan batas laju (rate limit)Exercise 12: Menerapkan fallback model Exercise 13: Selamat!