Let’s face it: knowing how to talk to AI isn’t optional anymore. And no, this isn’t some fleeting trend.
Prompt engineering is the new essential skill for anyone working with language models. If you want smart, relevant, and reliable outputs, you need the right approach - and that means understanding different types of prompts and knowing when to use them.
Here’s a breakdown of core prompting techniques I’ve been diving into in Kaggle’s intensive LLM course (learning everything from fine-tuning to Retrieval-Augmented Generation architecture), along with tips on how to get the most from each.
Prompt Engineering Prompt Types
System Prompting
What it does: Sets the foundational rules and goals for the model, essentially framing its "thinking" process.
Use it for: Scenarios where strict parameters are necessary—ideal for business processes, customer service, or standardized responses.
Example: "Classify movie reviews as positive, neutral, or negative. Only return the label in uppercase."
Role Prompting
What it does: Assigns a persona to the model, enabling it to respond with expertise or tone that matches the role.
Use it for: Creating interactive experiences, such as role-based customer support, virtual experts, or content guides.
Example: "I want you to act as a travel guide. I’ll write to you about my location, and you suggest three places to visit."
Contextual Prompting
What it does: Provides specific context to frame the model’s response.
Use it for: Task-specific content where the output needs to match a narrow context, such as generating blog topics, themed lists, or marketing copy.
Example: "Context: Writing for a blog about retro 80’s arcade video games. Suggest three article topics."
Zero-Shot Prompting
What it does: Gives a straightforward task description without examples.
Use it for: Simple queries needing quick answers without nuance—perfect for basic classification, short responses, or rapid-fire tasks.
Example: "Classify movie reviews as POSITIVE, NEUTRAL, or NEGATIVE."
Few-Shot Prompting
What it does: Uses examples to clarify the structure or style you want in the response.
Use it for: Complex or detailed formats needing consistency, such as JSON structures, lists, or step-by-step explanations.
Example:
"Provide product details in JSON format. Use this structure: Example 1: { 'name': 'Laptop', 'price': '$999', 'specs': { 'RAM': '8GB', 'Storage': '256GB SSD' } }\n\nExample 2: { 'name': 'Smartphone', 'price': '$699', 'specs': { 'Battery': '4000mAh', 'Camera': '12MP' } }\n\nCreate a JSON for a tablet."
Example Output:
{ 'name': 'Tablet', 'price': '$499', 'specs': { 'Screen': '10-inch', 'Battery': '5000mAh', 'Storage': '64GB' } }
Step-Back Prompting
What it does: Allows the model to ‘step back’ for a second look before finalizing an answer.
Use it for: Responses requiring a careful approach, especially useful for decision-making or reasoned judgment scenarios.
Example: "You are a consultant advising a small business on expanding into a new market. List the pros and cons of this expansion, then pause, review your list, and decide if the benefits outweigh the risks."
Example Output:
Initial Response:
Pros: Access to a new customer base, potential for increased revenue, brand visibility.
Cons: High entry costs, unknown competition, possible regulatory challenges.
Step-Back Review: "After reviewing, I see that the potential challenges in regulation and competition are substantial. It may be beneficial to conduct a market study first to assess readiness before proceeding."
Final Answer: "Recommendation: Conduct a market feasibility study to better understand the risks before investing in expansion."
Chain of Thought (CoT)
What it does: Encourages logical, step-by-step reasoning.
Use it for: Multi-step problems like math calculations, decision trees, or any answer requiring a methodical breakdown.
Example: "A school has 120 students, and each classroom can hold 15 students. Calculate how many classrooms are needed. Explain your reasoning step-by-step."
Example Output:
Step 1: "First, determine the total number of classrooms needed by dividing the total number of students by the number each classroom can hold."
Step 2: "120 students divided by 15 students per classroom equals 8 classrooms."
Final Answer: "The school needs 8 classrooms."
Self-Consistency Prompting
What it does: Generates multiple reasoning paths to confirm consistency in the answer.
Use it for: High-stakes responses where accuracy matters, such as medical suggestions, legal advice, or financial calculations. This approach increases reliability by cross-verifying answers.
Example: "A patient has symptoms of fatigue, weight gain, and sensitivity to cold. Based on this, provide two possible diagnoses, then re-evaluate each path to confirm which diagnosis is most consistent with the symptoms."
Example Output:
Path 1: "Symptoms align with hypothyroidism due to weight gain, fatigue, and sensitivity to cold, commonly associated with thyroid dysfunction."
Path 2: "Symptoms could also indicate anemia, as fatigue and sensitivity to cold are common, but weight gain is less typical."
Self-Consistency Check: "Upon re-evaluation, hypothyroidism is more consistent with all three symptoms. Anemia is less likely due to the weight gain."
Final Answer: "Most likely diagnosis: Hypothyroidism, given the full set of symptoms."
Tree of Thought (ToT) Prompting
What it does: Expands on Chain of Thought by exploring multiple reasoning paths simultaneously, allowing the model to assess various solutions or outcomes at each decision point.
Use it for: Complex, multi-step problems where intermediate choices significantly affect the final result. Ideal for tasks requiring sophisticated planning, such as strategic decision-making or resource allocation.
Example: "You are a travel planner creating the best route through a city, considering factors like traffic, weather, and transportation options. Explore multiple routes at each decision point and choose the optimal one."
Example Output:
Step 1: "Option A: Take the subway (fastest), Option B: Take a bus (less crowded), Option C: Walk (best for scenic views)."
Step 2: "Based on weather forecast (rain), subway is preferred."
Final Answer: "Optimal route: Subway for 10 stops, then a short walk to avoid traffic."
SO WHAT?
Prompt engineering is the backbone of effective AI interaction.
Instead of assuming AI is some “magic box,” let’s get down to the nuts and bolts: if you want reliable outputs, learn how to communicate. And don’t sleep on this... AI isn’t going anywhere. If anything, it’s only getting more "embedded" in every industry.
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