Prompting Techniques You Should Master in 2025

Executive summary

Prompting is no longer a niche skill — it’s expected knowledge.
Whether you’re building AI products, automating workflows, or just staying competitive, mastering prompt design makes the difference between generic answers and high-quality results.

This post covers 16 proven techniques, drawn from OpenAI, Anthropic, AI research cohorts, and my own experience. Each is illustrated with before/after examples.

---

Core prompting techniques

1. Role prompting

Give the model a persona or role to shape its reasoning.

Before:
Summarize this document: {content}

After:
You are a veteran product marketer with 20+ years of experience. Summarize this document: {content}

---

2. Context-first prompting

Don’t just state the task — explain why it matters and how success is measured.

Before:
Generate ten ideas for {problem}.

After:
We’re doing product discovery for {product}. One pain point is {customer_segment}. Generate 10 ideas that align with our {objective}.

---

3. Specify output format & constraints

Be explicit about the response style, schema, and limitations.

Example:

{
  "assumption": "...",
  "experiment": "...",
  "metric": "...",
  "expected": "...",
  "risk_mitigation": "..."
}

Constraints:

• Use markdown for experiment descriptions
• Include rollback criteria
• No subjective opinions

---

4. Few-shot prompting

Show the model examples of good and bad responses.

Example:

Q: What’s a good experiment idea?  
A: Run an A/B test with rollback criteria. ✅  

Q: What’s a bad experiment idea?  
A: Run a survey with vague questions. ❌  

Now generate 3 new experiment ideas.

---

5. Example-based prompting

Provide templates, patterns, or past artifacts.

Before:
Create user stories for {feature}

After:
Here are two sample user stories: {examples}. Create similar stories for {feature}.

---

6. Avoid leading questions

Ask open, balanced questions to prevent bias.

Before:
Why is {product} better than competitors?

After:
What are the strengths and weaknesses of {product} compared to competitors?

---

7. Raise the stakes

Frame the task as high-visibility to improve depth.

Example:
Imagine you’re reporting to the CEO. Provide a detailed analysis of {feedback}.

---

8. Plan–reflect–iterate

Encourage the model (or agent) to plan, reflect, and adapt.

Example:
Here are your objectives: {objectives}. Break this into steps, reflect after each, and continue until the problem is fully solved.

---

9. Tool-augmented prompting

Instruct agents to use tools in a specific order.

Example:

Goal: Decide whether to sunset {feature}.  
Always use tools in this sequence:  
1. CustomerSearch → find key customers  
2. CustomerUsageReport → check usage  

---

10. Chain-of-thought (CoT)

Force step-by-step reasoning.

Before:
Define a pricing strategy for {product}.

After:

1. Identify target segments  
2. Evaluate 3 models for each segment (pros/cons)  
3. Recommend best option with rationale  

---

11. Prompt chaining

Break complex tasks into sequential prompts.

• Prompt 1: Identify customer segments
• Prompt 2: For each, evaluate 3 monetization models
• Prompt 3: Recommend the best model

---

12. Clarify context

Ask the model to state assumptions or request missing info.

Example:
Before prioritizing {features}, list 3 assumptions that must hold true. If uncertain, ask clarifying questions.

---

13. Internal artifacts

Ask the model to create reusable artifacts (personas, glossaries) before performing tasks.

Example:
First, write a detailed persona for our mid-market user. Then use it to evaluate the proposed ideas.

---

14. Long-context strategies

For large context windows (100K–1M tokens):

• Put key instructions both at the start and end
• Decide whether to restrict to external docs only or allow fallback to model knowledge

---

15. Structured input

Use clear formats (Markdown, XML, JSON) to structure data.

Example (Markdown):

## 1. Executive summary
This document describes (...)
## 2. Business Goals
Our objective is (...)

Example (XML):

<examples>
  <example>ChatPRD</example>
  <example>aigents</example>
  <example>Grok</example>
</examples>

Example (JSON):

[
  { "id": 1, "title": "ChatGPT Cheat (...)", "content": "AI Agents: Plan, Reflect (...)" },
  { "id": 2, "title": "AI agent architectures (...)", "content": "AI Agents: Plan, Reflect (...)" }
]

---

16. Self-consistency sampling

Ask the model to generate multiple answers, then select or combine the best.

Example:
Generate 5 different solutions to {problem}. Compare and pick the most robust one.

---

Pitfalls

• Asking vague or overloaded prompts
• Forgetting constraints and schemas
• Over-relying on AI instead of integrating into workflows

---

➡️ Related service: Explore Services