Teknik Perancangan Prompt yang Efektif
Pelajari strategi untuk menulis prompt yang jelas, ringkas, dan efektif agar menghasilkan respons yang diinginkan dari LLM.
Teknik Perancangan Prompt yang Efektif adalah pelajaran AI Agents with LangChain & Autonomous Workflows gratis di CoddyKit. Ini adalah pelajaran 1 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar AI Agents with LangChain & Autonomous Workflows, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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!
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Teknik Perancangan Prompt yang Efektif” gratis?
Ya — teks lengkap “Teknik Perancangan Prompt yang Efektif” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus AI Agents with LangChain & Autonomous Workflows, upgrade ke CoddyKit PRO. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Teknik Perancangan Prompt yang Efektif”?
Pelajari strategi untuk menulis prompt yang jelas, ringkas, dan efektif agar menghasilkan respons yang diinginkan dari LLM. Kamu berlatih AI Agents with LangChain & Autonomous Workflows dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
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Tidak diperlukan pengalaman sebelumnya. AI Agents with LangChain & Autonomous Workflows di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 1 dari 4.
Berapa lama pelajaran “Teknik Perancangan Prompt yang Efektif” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran AI Agents with LangChain & Autonomous Workflows ini?
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Semua pelajaran dalam kursus ini
- Teknik Perancangan Prompt yang Efektif
- Mengintegrasikan LLM dengan LangChain
- Mengelola Parameter dan Biaya Model
- Penguraian dan Validasi Keluaran Terstruktur