AI Agents with LangChain & Autonomous Workflows · レッスン

効果的なプロンプト設計手法

LLMから望ましい応答を引き出す、明確で簡潔かつ効果的なプロンプトを作成する戦略を詳しく学びます。

「効果的なプロンプト設計手法」はCoddyKit上の無料AI Agents with LangChain & Autonomous Workflowsレッスンです。これはレッスン1/4です。下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。これはAI Agents with LangChain & Autonomous Workflows学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Guiding LLMs with Prompts

Welcome to Prompt Engineering! This lesson explores how to craft effective instructions, called prompts, to get the best responses from Large Language Models (LLMs).

Think of it as learning to speak the LLM's language. A well-designed prompt is key to unlocking powerful AI capabilities.

Be Clear, Be Specific

The first rule of prompt engineering is to be clear and specific. Vague instructions lead to vague or irrelevant answers. Tell the LLM exactly what you want.

Avoid ambiguity.
Use precise language.
Specify constraints (e.g., length, format).

Specificity in Action

See how a precise prompt yields a much better, structured result compared to a vague one. The LLM needs clear guidance!

def get_llm_response(prompt):
    if "3 bullet points" in prompt and "main benefits" in prompt:
        return "1. Automates tasks.\n2. Boosts creativity.\n3. Improves decision-making."
    elif "summarize" in prompt:
        return "Here is a summary of the article."
    return "I'm not sure what to do."

print("--- Vague Prompt ---")
vague_prompt = "Summarize the article about AI."
print("Prompt:", vague_prompt)
print("Response:", get_llm_response(vague_prompt))

print("\n--- Specific Prompt ---")
specific_prompt = "Summarize the article about AI in 3 concise bullet points, highlighting its main benefits."
print("Prompt:", specific_prompt)
print("Response:", get_llm_response(specific_prompt))

Give Your LLM a Role

Assigning a persona or role to the LLM can significantly influence its tone, style, and content. This helps the LLM adopt a specific perspective.

For example, asking it to "Act as a financial advisor" will result in a different response than "Act as a comedian."

Role-Playing Example

Observe how setting a role changes the LLM's output. The role provides essential context for generating appropriate responses.

def get_llm_response_with_role(role, query):
    if "pirate" in role:
        return f"Ahoy there! {query}, ye say? Here be the answer, matey! Arr!"
    elif "chef" in role:
        return f"Bonjour! As a chef, I can tell you about {query} with a culinary twist!"
    return f"Hello! Here's the answer to your query: {query}"

print("--- Standard Query ---")
print("Response:", get_llm_response_with_role("", "Tell me about gold."))

print("\n--- Pirate Role Query ---")
print("Response:", get_llm_response_with_role("You are a pirate captain.", "Tell me about gold."))

print("\n--- Chef Role Query ---")
print("Response:", get_llm_response_with_role("You are a Michelin star chef.", "Tell me about gold."))

Zero-Shot vs. Few-Shot

Zero-Shot Prompting: You give the LLM a task without any examples. It relies on its pre-trained knowledge.

Few-Shot Prompting: You provide a few input-output examples to guide the LLM on the desired format or pattern before asking for the main task. This is great for teaching specific styles.

Few-Shot Prompting in Action

Few-shot prompting helps the LLM understand a pattern or desired output format by showing it examples. Notice how the examples define the classification task.

# Few-Shot Prompt Structure Example
print("--- Few-Shot Example Prompt ---")
prompt_template = """
Classify the following items into 'Fruit' or 'Vegetable':

apple -> Fruit
carrot -> Vegetable
banana -> Fruit

Now classify these:
potato ->
tomato ->
"""
print(prompt_template)
print("\nThis prompt provides examples (apple, carrot, banana) to teach the LLM the classification pattern. It helps the LLM correctly classify 'potato' and 'tomato'.")

Structure Your Output

If you need the LLM's response in a specific structure, explicitly ask for it. This is crucial for integrating LLM outputs into other systems.

JSON: "Return as JSON with 'name' and 'age' keys."
Lists: "Provide 5 bullet points."
Tables: "Format as a Markdown table."

Iterate and Refine

Prompt engineering is an iterative process. Your first prompt might not be perfect. Always test, evaluate, and refine!

Test with various inputs.
Analyze unexpected or incorrect results.
Adjust instructions, add examples, or change the role.

Small tweaks can lead to significant improvements.

Crafting Better Prompts

You want an LLM to generate a recipe for a specific cuisine. Which techniques would be most useful to ensure a good, clear recipe?

Recap: Effective Prompting

You've learned core techniques for effective prompt design!

Clarity & Specificity: Be precise in your instructions.
Role-Playing: Assign a persona to guide the LLM's tone.
Few-Shot: Provide examples to teach patterns.
Output Formatting: Specify how you want the answer structured.
Iteration: Test and refine your prompts continuously.

Keep practicing these techniques to master guiding LLMs!

よくある質問

「効果的なプロンプト設計手法」レッスンは無料ですか？

はい。「効果的なプロンプト設計手法」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Agents with LangChain & Autonomous Workflowsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

「効果的なプロンプト設計手法」で何を学びますか？

LLMから望ましい応答を引き出す、明確で簡潔かつ効果的なプロンプトを作成する戦略を詳しく学びます。ブラウザで直接実行するハンズオンコードでAI Agents with LangChain & Autonomous Workflowsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

AI Agents with LangChain & Autonomous Workflowsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのAI Agents with LangChain & Autonomous Workflowsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。これはレッスン1/4です。

「効果的なプロンプト設計手法」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このAI Agents with LangChain & Autonomous Workflowsレッスンでコードを書いて実行できますか？

はい。すべてのAI Agents with LangChain & Autonomous Workflowsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

← AI Agents with LangChain & Autonomous Workflowsに戻る