Strategy12 min read Read

Prompt Engineering Best Practices 2025

PIEVOT Team•2025-12-18

Prompt engineering in 2025 is a different game than it was just a year ago. Models are smarter, more capable, and—paradoxically—require more sophisticated prompting to reach their full potential.

I've spent the last year testing these techniques across GPT-4, Claude 3, Gemini, and others. Here's what actually works in 2025.

The Shift: From Tricks to Architecture

In the early days of ChatGPT, prompt engineering was about "tricks"—magic words that seemed to unlock better responses. That era is over.

Today, prompt engineering is about architecture: building structured inputs that guide the AI through complex reasoning.

     2023 PROMPTING                    2025 PROMPTING
     
   ┌──────────────────┐            ┌──────────────────┐
   │  "Magic words"   │            │   Structured     │
   │  "Pretend to be..│     →      │   Reasoning      │
   │  "Think step..." │            │   Architecture   │
   └──────────────────┘            └──────────────────┘
          │                               │
          ▼                               ▼
     Clever tricks                 Systematic design

Best Practice #1: System Prompt Architecture

If you're not using system prompts (or Custom Instructions in ChatGPT), you're leaving massive performance on the table.

Why it matters:

The system prompt sets the "personality" and "rules" before any conversation starts. It's like hiring an employee and giving them a job description before their first task.

A strong system prompt template:

┌─────────────────────────────────────────────────────────┐
│                    SYSTEM PROMPT                         │
├─────────────────────────────────────────────────────────┤
│                                                          │
│  IDENTITY: You are [specific role with expertise]        │
│                                                          │
│  BEHAVIOR:                                               │
│  - Always [consistent behavior you want]                 │
│  - Never [behaviors to avoid]                            │
│  - When uncertain, [how to handle ambiguity]             │
│                                                          │
│  STYLE:                                                  │
│  - Tone: [specific tone guidance]                        │
│  - Format: [default formatting preferences]              │
│                                                          │
│  CONSTRAINTS:                                            │
│  - [Specific limitations or rules]                       │
│                                                          │
└─────────────────────────────────────────────────────────┘

Real example for a content creator:

"You are a senior content strategist who specializes in B2B SaaS marketing. You favor concise, punchy writing over long-winded explanations. You avoid corporate jargon and buzzwords. When I ask for content, default to actionable, specific advice over generic best practices. If you need more information to give a great answer, ask before proceeding."

Best Practice #2: Chain-of-Thought Prompting (Done Right)

"Think step by step" became a meme. But structured chain-of-thought still works—you just need to be more specific.

The evolution:

❌ 2023 (Overused): "Think step by step"

✅ 2025 (Effective): "Before answering, work through this explicitly:

1.What are the key factors to consider?

2.What are the tradeoffs between options?

3.What would a senior expert prioritize?

4.Given all that, what's your recommendation and why?"

When to use it:

   TASK COMPLEXITY SPECTRUM
   
   Simple ◄─────────────────────────────► Complex
   "What's 2+2?"                     "Design a system..."
        │                                   │
        ▼                                   ▼
   Skip chain-of-thought           USE chain-of-thought
   (unnecessary overhead)          (prevents shortcuts)

Best Practice #3: XML Tag Structuring

This technique exploded in 2024 thanks to Claude, but it works across all models. Using XML-like tags creates clear boundaries between different parts of your prompt.

Why tags work:

   WITHOUT TAGS (Muddled)            WITH TAGS (Clear)
   
   "Here's some context about       <context>
   my project, I'm building         I'm building a mobile
   a mobile app, and I need         app for local restaurants.
   help with the login flow,        </context>
   specifically for users           
   who don't have an account        <task>
   yet, can you help?"              Design the sign-up flow for
                                    new users without accounts.
        │                           </task>
        ▼                           
   AI has to parse meaning          <constraints>
   from a blob of text              - Must work on iOS and Android
                                    - Maximum 3 steps to complete
                                    </constraints>
                                    
                                         │
                                         ▼
                                    AI knows exactly what's what

Template you can use:

<role>
[Who the AI should be]
</role>

<context>
[Background information]
</context>

<task>
[Specific thing you need]
</task>

<constraints>
[Rules, limitations, requirements]
</constraints>

<format>
[How to structure the output]
</format>

<examples>
[Optional: sample outputs]
</examples>

Best Practice #4: Multi-Turn Refinement

Stop treating each prompt as a one-shot attempt. The best results come from intentional iteration.

The refinement pattern:

   ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
   │  PROMPT 1    │────▶│  RESPONSE 1  │────▶│  ANALYZE     │
   │  (Initial)   │     │  (Draft)     │     │  (What's     │
   └──────────────┘     └──────────────┘     │   missing?)  │
                                             └──────┬───────┘
                                                    │
   ┌──────────────┐     ┌──────────────┐            │
   │  RESPONSE 2  │◀────│  PROMPT 2    │◀───────────┘
   │  (Better)    │     │  (Refine)    │
   └──────────────┘     └──────────────┘
          │
          ▼
   [Continue until satisfied]

Powerful refinement prompts:

•"That's a good start. Now make it more [specific quality]"

•"Expand on point #3 with concrete examples"

•"Keep the structure, but make the language more casual"

•"What's the strongest counterargument to this, and how would you address it?"

•"If you had to cut this in half, what would you keep?"

Best Practice #5: Output Priming

You can guide the AI by starting its response for it. This is surprisingly effective for maintaining consistency.

How it works:

   YOUR PROMPT:
   "Explain the tradeoffs of microservices. 
    Begin your response with: 'The three key tradeoffs are:'"
   
   AI RESPONSE:
   "The three key tradeoffs are:
    1. [First tradeoff]..."  ← Follows your structure

Use cases:

•Force a specific structure: "Start with: 'Summary:'"

•Match a tone: "Start with: 'Look, here's the deal...'"

•Prevent hedging: "Start with your recommendation, then explain" (avoids "It depends...")

Best Practice #6: Persona Stacking

Advanced technique: Instead of one persona, combine multiple perspectives.

Example:

"Respond as a panel of three experts:

1.A startup founder who values speed and scrappiness

2.An enterprise architect who prioritizes scalability

3.A security specialist who focuses on risk

Each expert should give their perspective on: [your topic]"

This forces the AI to explore multiple angles instead of picking one.

Best Practice #7: Explicit Uncertainty Handling

Modern AI is trained to be helpful—sometimes too helpful. It will make up answers rather than admit uncertainty.

Fix this in your prompt:

✅ "If you're uncertain about any part of this, explicitly flag it with [UNCERTAIN]. Don't fill in gaps with assumptions."

✅ "Rate your confidence in each recommendation: High, Medium, or Low."

✅ "Distinguish between facts you're confident about and educated guesses."

Quick Reference: 2025 Best Practices

Practice	When to Use	Key Benefit
System Prompt Architecture	Every serious project	Consistent persona and rules
Structured Chain-of-Thought	Complex reasoning tasks	Prevents shortcuts, shows work
XML Tag Structuring	Multi-part prompts	Clear boundaries, less confusion
Multi-Turn Refinement	Quality-critical outputs	Iterative improvement
Output Priming	Consistency needed	Controls structure and tone
Persona Stacking	Complex decisions	Multiple perspectives
Uncertainty Handling	Accuracy matters	Reduces hallucinations

The Meta-Skill: Learning to Learn

The best prompt engineers in 2025 aren't just good at writing prompts—they're good at recognizing what works and systematizing it.

Build your own library:

•Save prompts that worked well

•Document why they worked

•Create templates for recurring tasks

•Share and learn from others

Prompt engineering is still evolving. The techniques that work today might be obsolete in a year. But the meta-skill—understanding how to communicate clearly with AI—will only become more valuable.

Share this article

PREVIOUSWhy Your ChatGPT Prompts Aren't Working