If you're still writing prompts like "write a blog post about AI," you're using a Ferrari like a wheelbarrow. It works, but you're missing 90% of the capability.

By 2025, prompt engineering has evolved from an art into a science with proven patterns. This isn't about finding magical phrases. It's about understanding how Large Language Models think and structuring your requests accordingly.

Before we dive into advanced patterns, if you're new to prompt engineering, start with my foundational guide on getting AI to do what you want. It covers the basics that make these patterns work.

Let me show you the patterns that separate amateur hour from production-ready AI systems.

Pattern 1: Zero-Shot Prompting (The Baseline)

This is your basic "just ask" approach. No examples, no hand-holding.

Classify the sentiment of this review as Positive, Negative, or Neutral:

"The new framework is fast but the documentation is terrible."

Sentiment:

When it works: Simple, well-defined tasks that the model has seen a million times during training (translation, basic summarization, common classifications).

When it fails: Nuanced tasks, specific formats, or anything requiring domain expertise.

Real example from the trenches: I once asked GPT-4 to "extract dates from this text" and it gave me dates in MM/DD/YYYY format. My Kenyan app expected DD/MM/YYYY. Spent 2 hours debugging before I realized the issue. Could've saved that with better prompting.

Pattern 2: Few-Shot Prompting (Show, Don't Tell)

Instead of explaining what you want, show examples. The model learns from the pattern.

The Structure: 

[Task instruction]

Example 1:
Input: [...]
Output: [...]

Example 2:
Input: [...]
Output: [...]

Now do this one:
Input: [...]
Output:

Practical Example: Extracting Structured Data

Extract event name and date from text. Return JSON.

Input: "Team standup is scheduled for March 15th at 9am"
Output: {"event": "Team standup", "date": "2025-03-15", "time": "09:00"}

Input: "Don't forget the Christmas party on Dec 25!"
Output: {"event": "Christmas party", "date": "2025-12-25", "time": null}

Input: "Product launch happening next Friday, January 20th, afternoon"
Output: {"event": "Product launch", "date": "2025-01-20", "time": "afternoon"}

Now extract from: "Board meeting on the 3rd of July at 2pm"
Output:

Why it works: The model sees the pattern: informal text → structured JSON. It even learns to handle missing data (time: null) and ambiguous times ("afternoon").

Pro Tip: Make your examples cover EDGE CASES. Show it what to do with:

  • Missing information
  • Ambiguous inputs
  • Different formats
  • Negative examples (what NOT to do)

Pattern 3: Chain-of-Thought (CoT) - Think Before You Speak

This is the breakthrough that changed everything. Instead of asking for an answer, ask for the REASONING first.

The Magic Phrase: "Let's think step by step."

Example: Math Word Problem

Bad (zero-shot): 

A developer writes 50 lines of code per hour. They work 6 hours but spend 20% of time in meetings. How many lines did they write?

Answer: 300
Wrong. The model rushed to an answer.

Good (Chain-of-Thought): 

A developer writes 50 lines of code per hour. They work 6 hours but spend 20% of time in meetings. How many lines did they write?

Let's think step by step:

1. Total time: 6 hours
2. Meeting time: 20% of 6 = 1.2 hours
3. Actual coding time: 6 - 1.2 = 4.8 hours
4. Lines per hour: 50
5. Total lines: 50 × 4.8 = 240 lines

Answer: 240 lines

Correct!

When to use CoT:

  • Math problems
  • Logic puzzles
  • Code debugging
  • Complex decision-making
  • Anything where the first obvious answer is often wrong

Real-world use case: I use this for code reviews. Instead of "review this code," I prompt:

Review this code step by step:
1. Check for security vulnerabilities
2. Check for performance issues
3. Check for readability problems
4. Suggest improvements

[Code here]

The model is forced to be methodical, not just vibe-check the code.

Pattern 4: Tree-of-Thought (ToT) - Explore Multiple Paths

Chain-of-Thought is one path. Tree-of-Thought explores MULTIPLE paths and picks the best one.

Think of it like this:

  • CoT: Take Thika Road to town
  • ToT: Consider Thika Road, Mombasa Road, and Eastern Bypass. Evaluate traffic on each. Pick the fastest.

The Pattern:

You need to [solve problem X].

Step 1: Generate 3 different approaches to solve this.
Step 2: For each approach, list pros and cons.
Step 3: Evaluate which approach is most robust.
Step 4: Implement the chosen approach in detail.

Real Example: System Architecture Decision

Design a notification system that handles 1M requests/minute.

Step 1 - Generate 3 different approaches:

Approach A: Redis Pub/Sub
- Pros: Fast, simple, good for real-time
- Cons: No persistence, can lose messages, scaling is manual

Approach B: Apache Kafka
- Pros: Persistent, highly scalable, battle-tested
- Cons: Complex setup, overkill for simple use cases, resource-heavy

Approach C: AWS SNS/SQS
- Pros: Managed, scales automatically, integrates with AWS
- Cons: Vendor lock-in, more expensive, requires AWS knowledge

Step 2 - Given that we have:
- 3 backend engineers (no dedicated DevOps)
- Budget constraints
- Need reliability > cutting-edge features

Best choice: Approach C (AWS SNS/SQS)

Step 3 - Detailed implementation:
[Model provides full architecture]

When to use ToT:

  • High-stakes decisions
  • Complex problems with multiple valid solutions
  • When you need to explain WHY you chose a solution
  • Architecture and design discussions

Pattern 5: The CO-STAR Framework (Structure Your Prompts)

Stop writing prompts like stream-of-consciousness WhatsApp messages. Use a framework.

CO-STAR:

  • Context: Background information
  • Objective: What you want
  • Style: Tone and voice
  • Tone: Attitude (formal, casual, etc.)
  • Audience: Who will read this
  • Response format: How you want the output

Example:

# CONTEXT
You are a senior backend engineer at a fintech startup. We're migrating from a monolith to microservices.

# OBJECTIVE
Write a technical design document for our user authentication service.

# STYLE
Professional, technical, detailed.

# TONE
Cautious and security-focused. This handles money.

# AUDIENCE
The document will be reviewed by:
- Junior developers who will implement it
- Security team who will audit it
- CTO who will approve it

# RESPONSE FORMAT
Use this structure:
1. Overview (2-3 sentences)
2. Requirements
3. System Design (include data flow diagram description)
4. Security Considerations
5. Testing Strategy
6. Rollout Plan

Compare that to: "Write a design doc for auth service."

One gets you production-ready documentation. The other gets you... something.

These structured prompts work especially well when building conversational AI agents with LangChain, where system prompts define agent behavior.

Pattern 6: Self-Consistency (Ask Multiple Times, Take the Best)

Models are probabilistic. Same prompt can give different answers. Use this to your advantage.

The Technique:

  1. Ask the same question 3-5 times
  2. Compare answers
  3. Take the most common answer (majority vote)

When it matters: High-stakes decisions, math problems, code generation.

Implementation:

def get_consistent_answer(prompt, n=3):
    answers = []
    for _ in range(n):
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.7  # Some randomness
        )
        answers.append(response.choices[0].message.content)

    # Return most common answer
    from collections import Counter
    return Counter(answers).most_common(1)[0][0]

Pattern 7: Reflexion (Make AI Critique Itself)

Two-step process:

  1. Generate answer
  2. Ask model to critique its own answer and improve it

Example:

# Step 1: Get initial response
prompt = "Write a Python function to validate Kenyan phone numbers"
initial = get_response(prompt)

# Step 2: Self-critique
critique_prompt = f"""
You wrote this function:

{initial}

Review it for:
1. Edge cases it misses
2. Security vulnerabilities
3. Performance issues
4. Code clarity

Then provide an improved version.
"""

improved = get_response(critique_prompt)

Real result: The initial version usually misses edge cases. The improved version handles +254, 0, missing leading zeros, etc.

This self-critique approach is especially powerful in multi-agent systems where reviewer agents provide feedback loops.

Pattern 8: Role-Based Prompting (Be Specific About Expertise)

Don't ask "a helpful assistant." Ask a SPECIFIC expert.

Weak: 

Explain how to optimize database queries.

Strong: 

You are a senior database administrator with 10 years of PostgreSQL experience.

A junior developer asks: "Why is this query slow?"

Query: SELECT * FROM orders WHERE user_id = 123 AND created_at > '2024-01-01'

Explain the problem and solution in a way they'll understand and remember.

The second one gets you:

  • Index recommendations
  • EXPLAIN ANALYZE suggestions
  • Real-world debugging tips
  • Mentorship tone

The first one gets you... generic advice.

Pattern 9: Constrained Generation (Set Boundaries)

AI loves to write essays. Sometimes you need exactly 3 bullet points.

Examples:

Explain React hooks in EXACTLY 2 sentences.

List 5 reasons, no more, no less.

Response must be valid JSON. No additional text.

Your response must be under 100 words. Be concise.

Why this matters: Token costs, UI constraints, user attention span.

Pattern 10: The Refusal Prime (Admit When You Don't Know)

Add this to your system prompt:

If you don't have enough information to give a high-quality answer, ASK CLARIFYING QUESTIONS instead of guessing.

If you're uncertain, say so. Never make up facts.

Example dialogue:

Without prime: User: "How do I configure the flux capacitor?" AI: "To configure the flux capacitor, first access the settings panel..." (HALLUCINATING)

With prime: User: "How do I configure the flux capacitor?" AI: "I'm not familiar with a 'flux capacitor' in current technology. Are you referring to:

  1. A component in a specific framework?
  2. A fictional device from Back to the Future?
  3. An internal term at your company?

Can you provide more context?"

The Pattern Selection Flowchart

Is the task simple and common?
→ YES: Zero-shot
→ NO: ↓

Do you have examples of good outputs?
→ YES: Few-shot
→ NO: ↓

Does the task require reasoning?
→ YES: Chain-of-Thought
→ NO: ↓

Are there multiple valid approaches?
→ YES: Tree-of-Thought
→ NO: ↓

Is this high-stakes and worth extra API calls?
→ YES: Self-Consistency + Reflexion
→ NO: Improve your prompt structure (CO-STAR)

Real-World Example: Code Generation

Level 1 (Beginner): 

Write a function to sort an array

Level 2 (Competent): 

Write a Python function to sort an array of dictionaries by the 'date' key.
Include error handling and type hints.

Level 3 (Expert): 

# CONTEXT
You are a Python expert reviewing code for a production system.

# TASK
Write a function that sorts an array of user objects by registration date.

# REQUIREMENTS
- Handle edge cases (None values, invalid dates)
- Use type hints
- Include docstring (Google style)
- Optimize for lists with 10k+ items
- Include 3 unit test examples

# CONSTRAINTS
- Python 3.10+
- Use only standard library (no external deps)

Let's think step by step:
1. First, identify edge cases
2. Then, write the function
3. Finally, write the tests

Guess which one gives you production-ready code?

For RAG applications specifically, check out how these prompting patterns apply to building production RAG systems with retrieval-augmented generation.

To see these patterns in action, explore how they're used in practical AI API integrations or building AI chatbots from scratch.

The Bottom Line

Prompt engineering in 2025 isn't about magic words. It's about:

  1. Specificity - Say exactly what you want
  2. Structure - Use frameworks like CO-STAR
  3. Examples - Show, don't just tell (Few-shot)
  4. Reasoning - Force step-by-step thinking (CoT, ToT)
  5. Constraints - Set boundaries
  6. Verification - Use self-consistency and reflexion

Start with zero-shot. If it fails, add examples (few-shot). If the logic is complex, use Chain-of-Thought. If you need exploration, use Tree-of-Thought.

And always, ALWAYS test your prompts with edge cases before deploying to production.

Better prompts = better results = cheaper API bills.