Técnicas eficazes de design de prompts
Aprofunde-se em estratégias para escrever prompts claros, concisos e eficazes que produzam as respostas desejadas dos LLMs.
Técnicas eficazes de design de prompts é uma aula grátis de AI Agents with LangChain & Autonomous Workflows no CoddyKit. Esta é a aula 1 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de AI Agents with LangChain & Autonomous Workflows, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
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!
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O que vou aprender em “Técnicas eficazes de design de prompts”?
Aprofunde-se em estratégias para escrever prompts claros, concisos e eficazes que produzam as respostas desejadas dos LLMs. Você pratica AI Agents with LangChain & Autonomous Workflows com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar AI Agents with LangChain & Autonomous Workflows?
Nenhuma experiência prévia é necessária. AI Agents with LangChain & Autonomous Workflows no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 1 de 4.
Quanto tempo leva a aula “Técnicas eficazes de design de prompts”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de AI Agents with LangChain & Autonomous Workflows?
Sim. Cada aula de AI Agents with LangChain & Autonomous Workflows inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Técnicas eficazes de design de prompts
- Integrando LLMs ao LangChain
- Gerenciando parâmetros e custos dos modelos
- Análise e validação de saída estruturada