Aug 16 • 💡 Prompt Library
Top Prompt Engineering Techniques (with Real Examples & Outcomes)
1. Zero-Shot Prompting
Definition: Directly instruct the AI to perform a task with no examples.
Why it works: Relies on the model’s general knowledge to understand your intent.
Example Prompt:
“Classify the following movie review as Positive, Negative, or Neutral. Review: ‘The storyline was engaging but the dialogue felt flat.’ Sentiment: ”Outcome: Correctly labels sentiment as Neutral, using just minimal guidance.
2. Few-Shot Prompting
Definition: Include a few examples to demonstrate the format or style you expect.
Why it works: Helps the model mimic structure and tone.
Example Prompt:
“Example: A 'baku' is a large blue flightless bird. ‘We saw many bakus on our trip.’ Now: ‘Write a short story about a baku that ended up on a spaceship bound for Mars." Uses the pattern to generate a coherent, stylistically consistent story.
3. Chain-of-Thought (CoT) Prompting
Definition: Encourage step-by-step reasoning to solve complex tasks.
Why it works: Breaks tasks into smaller logical parts, improving accuracy.
Example Prompt:
"I had 8 marbles, gave 3 away, then found 4 more. How many do I have now? Think step by step."
Outcome:
- You started with 8.
- Gave away 3 → 5.
- Found 4 more → 9.Answer: 9.
4. Meta Prompting
Definition: Guide with abstract, structured steps instead of specifics—great for repeatable logic. Why it works: Keeps prompts flexible and reusable across tasks.
Example Prompt:
“Step 1: Define variables. Step 2: Apply formula. Step 3: Simplify and solve.”
Outcome: Useful for generic coding problems or analytical processes.
5. Self-Consistency Prompting
Definition: Generate multiple reasoning paths (CoT) and select the most consistent answer.
Why it works: Combats reasoning errors and improves reliability.
Outcome: Produces more accurate results by cross-checking across logic chains.
6. Context-First Prompting (Agent Focused)
Definition: For AI agents, provide rich context to guide decision-making. Why it works: Agents perform better when they have clear, relevant background.
Takeaway: Load your prompt with the user’s situation or data before asking for actions.
7. Self-Ask & Step-Back Prompting
Definition:
- Self‑Ask: Break down hard queries into sub‑questions.
- Step‑Back: Ask a general prompt first, then refine with focused follow-ups.
- Example (Self-Ask):
“Should I pursue a master’s in data science? First, analyze career prospects, costs, and market demand. Then, recommend.”
Facilitates deeper reasoning and more actionable feedback.
8. Role-Based and Contextual Prompting
Definition: Assign a persona and specific context to shape responses.
Why it works: Provides clarity about tone, audience, and style.
Example Prompt:
“You are a friendly travel guide explaining tariffs to beginners in a TV interview style. Include one positive and one negative example.”
Outcome: Produces clear, audience-tailored answer.
9. Multi-Shot + Chain-of-Thought & Structured Prompting (Anthropic Style)
Definition:
- Use multi-shot examples to anchor formatting and style.
- Apply CoT to guide reasoning.
- Assign roles, provide explicit structure, and allow “I don’t know” to reduce hallucinations.
- Outcome: High precision, clarity, and truthfulness in responses.
Quick Reference Table
Technique Core Idea Perfect For Zero-Shot No examples—just clear instructions Quick tasks, simple classification Few-Shot Provide examples of desired output Style-sensitive or nuanced tasks Chain-of-Thought (CoT) Step-by-step reasoning Math, logic, multi-step decision-making Meta Prompting Abstract structured guidance Reusable logic workflows or coding problems Self-Consistency Generate multiple reasoning paths Accuracy-sensitive tasks Context-First (Agents) Provide rich scenario or data ahead Agent workflows or situational tasks Self-Ask / Step-Back Decompose then refine Complex queries or layered analysis Role-Based Context Define persona/audience and context Tone-specific or professional communication Multi-Shot + CoT Combine examples, roles, logic chains, structure High-stakes tasks, fact-based output
Featured Real-World Example: Agent Workflow Hack
Scenario: Automating customer email triage.
Approach:
- Role-Based Context:
- Few-Shot Examples:
- Chain-of-Thought Prompting:
- Self-Ask (if needed):
- Self-Consistency: Run multiple variations and pick the top category.
Outcome: Agent reliably routes emails while identifying ambiguous cases for human review.
Final Thoughts
Please visit the Audio podcast and video listed below.
- Use Clear, Structured Instructions—whether it's via roles, few-shot examples, or CoT, clarity wins.
- Experiment Iteratively—test variations like zero- vs few-shot or meta prompts to see what fits best.
- Combine multiple techniques for complex tasks—especially when reliability and precision matter.
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Top Prompt Engineering Techniques (with Real Examples & Outcomes)
