TL;DR: What You'll Learn

• Text AI tools share core prompting principles despite different architectures and training approaches

• Three prompt patterns solve 80% of text generation needs: Structured Output, Chain-of-Thought, and Multi-Perspective

• Model-specific strengths matter: ChatGPT excels at structured tasks, Claude at nuanced reasoning, Gemini at multimodal integration

• Templates provide starting points but require adaptation to your specific context and constraints

• Use the evaluation scorecard to diagnose prompt failures and systematically improve results

Most people approach text AI the same way they approach search engines. They type a question, get an answer, and accept whatever the model generates.

This works for simple queries. It fails for complex tasks where output quality, tone, and structure matter.

The difference between mediocre and excellent text AI outputs isn't the model—it's the prompt structure. ChatGPT, Claude, and Gemini all produce poor results when given incomplete instructions. They all produce excellent results when prompted with clarity and precision.

This article provides battle-tested templates for the most common text AI tasks, explains when to use each pattern, and shows how to adapt prompts across different models.

Understanding Text AI Differences

ChatGPT, Claude, and Gemini share more similarities than differences, but understanding where they diverge helps you choose the right tool and adjust your prompting approach.

ChatGPT (GPT-4, GPT-4o, o1)

Strengths: Structured output, code generation, step-by-step processes, factual recall when given context, consistency across multiple generations.

Prompt approach: Works well with explicit formatting instructions, bullet points, numbered steps, and clear structural requirements. Responds effectively to role framing and specific constraints.

Best for: Technical documentation, code explanation, structured analysis, process documentation, data transformation tasks.

Claude (Sonnet, Opus)

Strengths: Nuanced reasoning, ethical considerations, complex analysis, handling ambiguity, maintaining context over long conversations, thoughtful critique.

Prompt approach: Benefits from conversational framing, explicit thinking steps, multi-perspective analysis. Excels when asked to consider tradeoffs or explain reasoning.

Best for: Strategic analysis, ethical evaluation, content critique, research synthesis, complex decision support, nuanced writing.

Gemini (2.0 Flash, Pro)

Strengths: Multimodal integration (text + images), real-time information access, broad general knowledge, fast processing, handling diverse input types.

Prompt approach: Works well with visual context, benefits from explicit output format specification, handles mixed-media inputs naturally.

Best for: Tasks requiring visual understanding, research with current information needs, multimodal content creation, quick general queries.

These distinctions guide tool selection, but the core prompting principles remain constant across all three.

Three Foundational Prompt Patterns

Most text AI tasks fall into one of three categories. Master these patterns and you can handle 80% of common use cases.

Pattern 1: Structured Output Prompts

When to use: You need consistent format, specific organization, or output that integrates into workflows (reports, documentation, data transformation).

Core principle: Specify exact structure upfront. The AI needs to know what template to fill rather than creating structure from scratch.

Template structure:

You are [role with relevant expertise].

Create [specific output type] with the following structure:
- [Section 1]: [requirements]
- [Section 2]: [requirements]
- [Section 3]: [requirements]

Requirements:
- [Length constraints]
- [Format specifications]
- [Tone/style direction]
- [What to include/exclude]

Context: [background information needed to complete task]

Example—Business Report:

Prompt: "You are a business analyst preparing executive briefings. Create a competitive analysis report with this structure:

1. Executive Summary (100 words): Key findings and strategic implications

2. Market Position: Our strengths/weaknesses vs competitors

3. Competitive Threats: Emerging risks and their likelihood

4. Opportunities: Gaps we can exploit

5. Recommendations: 3 specific actions with expected outcomes

Requirements:

• Evidence-based: cite specific examples

• Actionable: every insight must connect to a decision

• Concise: use bullet points, avoid narrative

• Skeptical: challenge assumptions, note uncertainty

Context: We're a B2B SaaS company ($50M ARR) competing against established enterprise players and VC-backed startups. Recent product launch underperformed expectations."

Why this works: Explicit structure prevents rambling. Requirements specify quality standards. Context grounds the analysis in reality. The AI knows exactly what to produce.

Cross-model adaptation:

• ChatGPT: Handles this pattern natively, follows structure precisely

• Claude: Add "Think through each section carefully, noting tradeoffs" to encourage deeper analysis

• Gemini: Works well as-is, can incorporate uploaded competitive data or screenshots

Pattern 2: Chain-of-Thought Prompts

When to use: Complex reasoning tasks, problem-solving, analysis where you need to verify logic, situations where the path matters as much as the conclusion.

Core principle: Make reasoning explicit. Ask the AI to show its work rather than jumping to conclusions.

Template structure:

You are [role with analytical expertise].

Analyze [problem/question] by thinking through it step by step:

Step 1: [What to examine first]
Step 2: [What to consider next]
Step 3: [What to evaluate]
Step 4: [What conclusion to draw]

For each step:
- State your reasoning explicitly
- Note assumptions you're making
- Identify uncertainties or gaps
- Explain how it connects to the next step

Context: [relevant background]
Constraints: [limitations to consider]

Example—Technical Decision:

Prompt: "You are a technical architect evaluating infrastructure decisions. We need to choose between serverless and containerized deployment for a new service.

Think through this decision step by step:

Step 1: Identify the key factors that should influence this decision (cost, scalability, complexity, team expertise)

Step 2: Analyze our specific context:

• Expected traffic: 1M requests/day with occasional 10x spikes

• Team: 5 engineers, strong with containers, limited serverless experience

• Budget: $5K/month infrastructure

• Timeline: 3 months to production

Step 3: Evaluate tradeoffs for each option given our constraints

Step 4: Make a recommendation with clear reasoning

For each step, state assumptions and note where you're uncertain. If you need clarification to provide better analysis, ask."

Why this works: Explicit steps prevent shallow analysis. Asking for assumptions surfaces potential blind spots. Permission to ask questions encourages appropriate uncertainty rather than false confidence.

Cross-model adaptation:

• ChatGPT: May rush through steps; add "Take your time with each step" to slow down analysis

• Claude: Excels at this pattern; naturally considers nuances and tradeoffs

• Gemini: Works well; can incorporate technical documentation or architecture diagrams if uploaded

Pattern 3: Multi-Perspective Prompts

When to use: Subjective decisions, content that needs to resonate with different audiences, avoiding blind spots, creative exploration.

Core principle: Force consideration of multiple viewpoints. The AI generates richer outputs when prompted to think from different angles.

Template structure:

You are [role with broad perspective].

Analyze [topic/question] from multiple perspectives:

Perspective 1: [stakeholder/viewpoint]
- What matters most from this angle?
- What concerns or objections arise?
- What would success look like?

Perspective 2: [different stakeholder/viewpoint]
- [same questions]

Perspective 3: [contrasting viewpoint]
- [same questions]

After examining all perspectives:
- Identify common ground
- Note irreconcilable conflicts
- Suggest approaches that balance competing concerns

Context: [situation details]

Example—Product Strategy:

Prompt: "You are a product strategist advising on feature prioritization. We're debating whether to build advanced analytics features or improve core workflow automation.

Analyze from these perspectives:

Perspective 1: Enterprise customers ($100K+ ARR accounts)

• What do they value most?

• What would make them churn?

• Which feature strengthens retention?

Perspective 2: Small business customers ($5K-20K ARR accounts)

• What drives their buying decisions?

• What complexity can they handle?

• Which feature improves their experience most?

Perspective 3: Sales team

• What do prospects ask for?

• What objections come up?

• Which feature closes more deals?

Perspective 4: Engineering team

• What's the technical cost/complexity?

• What creates long-term maintenance burden?

• Which aligns with our technical direction?

After examining all angles, identify: (1) where perspectives align, (2) key conflicts, (3) recommendation that balances concerns.

Context: Series B SaaS company, 1,000 customers, 15-person engineering team, pressure to grow ARR 3x this year."

Why this works: Forcing multiple perspectives prevents tunnel vision. Identifying conflicts explicitly makes decision criteria clear. The context grounds analysis in real constraints.

Cross-model adaptation:

• ChatGPT: Handles multiple perspectives well but may not emphasize conflicts; add "Highlight where perspectives fundamentally conflict"

• Claude: Naturally strong at this; tends to consider nuances and ethical implications

• Gemini: Works well; can incorporate customer feedback data or sales reports if available

Ready-to-Use Templates by Category

Business & Strategy

Market Research Summary

You are a market research analyst synthesizing findings for executives.

Analyze [market/trend/competitor] and create a brief with:

1. Key Insight (50 words): The one thing leadership needs to know
2. Supporting Evidence (100 words): Data and examples backing the insight
3. Strategic Implications (100 words): What this means for our business
4. Recommended Actions (3 bullet points): Specific next steps

Tone: Direct, evidence-based, actionable. Avoid: speculation, buzzwords, generic advice.

Context: [Your industry, company size, competitive position]
Source material: [Data or information to analyze]

Decision Framework

You are a strategic consultant helping evaluate a complex decision.

We need to decide: [specific decision]

Create a decision framework:

1. Decision criteria (what factors should weigh most?)
2. Options analysis (evaluate each option against criteria)
3. Risk assessment (what could go wrong with each path?)
4. Recommendation (with clear reasoning)

For each section:
- Show your reasoning explicitly
- Note assumptions
- Identify what additional information would change your analysis

Context: [Relevant background, constraints, stakeholders]
Timeline: [When decision needed]

Content Creation

Email Sequence

You are a marketing copywriter specializing in B2B email campaigns.

Create a [number]-email sequence for [specific goal].

For each email:

Subject line: [Under 50 characters, avoid spam triggers]
Preview text: [First line that shows in inbox]
Body: [150-200 words]
- Opening hook (1 sentence addressing specific pain point)
- Value proposition (2-3 sentences on benefit)
- Social proof or credibility signal (1 sentence)
- Single clear CTA
- Closing that maintains relationship

Tone: [Specify: conversational, professional, casual, authoritative]
Audience: [Who they are, what they care about, what they're skeptical of]

Avoid: Hype language, multiple CTAs, selling without context, generic benefits

Context: [Product/service details, customer pain points, competitive positioning]

Article Outline

You are a content strategist creating outlines for editorial content.

Create a detailed outline for: [article topic]

Structure:
1. Hook (why this matters now, what problem it solves)
2. Core sections (3-5 main points)
   - For each section: key argument, supporting evidence needed, examples to include
3. Actionable takeaway (what reader should do with this information)

Requirements:
- Each section should build on previous
- Include transition logic between sections
- Note where examples/data are needed
- Identify potential reader objections to address

Target audience: [Who they are, expertise level, what they're trying to accomplish]
Tone: [Authoritative, conversational, technical, accessible - specify]
Length target: [Word count]

Technical & Development

Code Explanation

You are a senior developer explaining code to [junior developers / non-technical stakeholders / other developers].

Explain what this code does: [paste code]

Structure your explanation:

1. High-level purpose (one sentence: what problem this solves)
2. Main components (break down into logical pieces)
3. Key logic (explain non-obvious decisions)
4. Potential issues (edge cases, performance considerations, technical debt)

Explanation level: [Beginner / Intermediate / Advanced]
Focus on: [Understanding flow / Learning patterns / Identifying improvements]

Avoid: Line-by-line narration, assuming knowledge of [specific concepts]

Technical Documentation

You are a technical writer creating documentation for developers.

Document [feature/API/system] with this structure:

1. Overview (what it does, why it exists)
2. Quick Start (minimal example to get working)
3. Core Concepts (3-5 key things users must understand)
4. Common Use Cases (with code examples)
5. Configuration Reference (options and their effects)
6. Troubleshooting (common issues and solutions)

For each section:
- Write for developers who are [skill level]
- Include working code examples
- Link to related documentation
- Note what's optional vs required

Technical depth: [How much detail to include]
Assumptions: [What knowledge readers already have]

Analysis & Research

Literature Review Summary

You are an academic researcher synthesizing literature for [your field].

Review and synthesize findings from these sources: [list sources or paste abstracts]

Create a synthesis structured as:

1. Research Question Context (what gap this addresses)
2. Methodological Approaches (how researchers studied this)
3. Key Findings (organized by theme, not by paper)
4. Contradictions or Disagreements (where sources conflict)
5. Research Gaps (what remains unknown)
6. Implications (so what? why does this matter?)

For each point:
- Cite specific sources
- Note confidence level (strong consensus vs emerging theory)
- Identify methodological limitations

Avoid: Summarizing each paper separately, accepting claims uncritically, ignoring contradictions

Target: [Academic audience / General educated audience / Practitioners]

Data Analysis Report

You are a data analyst creating an executive report.

Analyze this data: [paste data or describe dataset]

Create a report with:

1. Executive Summary (3-5 bullet points: key findings only)
2. Methodology (how you analyzed the data, limitations)
3. Findings (organized by importance, not by analysis order)
   - For each finding: what it means, why it matters, confidence level
4. Visualizations (describe what charts/graphs would clarify findings)
5. Recommendations (specific actions supported by data)

Requirements:
- Lead with conclusions, not process
- Quantify magnitude (don't just say "increased"—say "increased 23%")
- Note uncertainty (don't overstate confidence)
- Connect findings to business decisions

Context: [What question this analysis answers, who needs the answer, what decisions depend on it]

The Prompt Evaluation Scorecard

Use this diagnostic tool to identify why prompts fail and how to improve them.

Component Checklist:

☐ Role Clarity: Does the prompt specify what expertise or perspective to apply?

• Missing: AI uses generic knowledge patterns

• Present: AI activates relevant specialized knowledge

☐ Task Specificity: Is the output format and structure explicit?

• Missing: AI invents structure based on common patterns

• Present: AI knows exactly what to create

☐ Context Completeness: Have you provided necessary background, constraints, and success criteria?

• Missing: AI makes assumptions that may not align with your needs

• Present: AI grounds output in your specific situation

☐ Style Direction: Is the tone, voice, and approach clear?

• Missing: AI defaults to neutral formal style

• Present: AI matches your required communication style

☐ Constraints Definition: Are limits, requirements, and prohibitions specified?

• Missing: AI may produce unusable format or violate requirements

• Present: AI respects boundaries that matter

Failure Diagnosis:

If output is too generic: Add more specific role context and constraints If output is wrong format: Make task structure more explicit If output misses the point: Provide better context about goals and audience If output sounds wrong: Add detailed style direction with examples If output is unusable: Define technical constraints and requirements

Iteration Strategy:

When output doesn't meet needs:

1. Don't rewrite the entire prompt randomly

2. Identify which specific component failed

3. Add or refine only that component

4. Test and evaluate

5. Continue until output quality meets requirements

For detailed diagnostic techniques, see AI Prompt Evaluation Checklist: Diagnose Why Your Prompts Fail & Fix Them Fast.

Model-Specific Optimization Techniques

ChatGPT Optimization

Leverage strengths:

• Explicit formatting instructions work extremely well

• Responds effectively to structured prompts with clear sections

• Handles code and technical content naturally

• Maintains consistency across multiple generations

Prompting adjustments:

• Use numbered steps, bullet points, section headers for clarity

• Specify output format explicitly (JSON, markdown, HTML, plain text)

• Include examples of desired format when structure is complex

• Request specific length (word count, character count) for consistency

Common issues and fixes:

• Verbose: Add "Be concise—maximum [X] words per section"

• Generic: Add more specific role context and concrete examples

• Inconsistent: Create explicit template and reference it in prompt

Claude Optimization

Leverage strengths:

• Excels at nuanced analysis and considering multiple perspectives

• Strong at explaining reasoning and identifying uncertainties

• Maintains thoughtful tone naturally

• Handles complex ethical considerations well

Prompting adjustments:

• Encourage thinking steps: "Think through this carefully, considering..."

• Request reasoning: "Explain your logic for each recommendation"

• Invite uncertainty: "Note where you're uncertain or need more information"

• Use conversational framing for better engagement

Common issues and fixes:

• Too cautious: Add "Be direct—I need clear recommendations despite uncertainty"

• Overthinking: Add "Focus on practical implications, not theoretical edge cases"

• Verbose: Add "Prioritize clarity over comprehensiveness"

Gemini Optimization

Leverage strengths:

• Handles multimodal inputs (text + images) naturally

• Access to current information when needed

• Fast processing for quick iterations

• Strong at general knowledge queries

Prompting adjustments:

• Include visual context when relevant (upload images, diagrams, screenshots)

• Specify when current information is needed vs historical knowledge

• Leverage broad knowledge base for cross-domain connections

• Use for rapid prototyping before refinement in other models

Common issues and fixes:

• Surface-level: Add depth requirements: "Go beyond overview—analyze implications"

• Inconsistent with visuals: Be explicit about what elements in image matter

• Generic: Provide more specific context about your unique situation

Common Mistakes to Avoid

Mistake 1: Template Misuse

Problem: Copying templates without adapting them to your specific context.

A template for "business email" won't work if you don't specify your audience, goal, and constraints. Templates are starting points, not finished prompts.

Fix: Always customize the context section. Replace bracketed placeholders with your actual situation. Add constraints specific to your needs.

Mistake 2: Model Mismatching

Problem: Using the wrong tool for the task.

ChatGPT for nuanced ethical analysis (Claude's strength), Claude for rapid structured output iterations (ChatGPT's strength), or Gemini without leveraging its multimodal capabilities.

Fix: Match model strengths to task requirements. For complex analysis needing careful reasoning, use Claude. For structured technical output, use ChatGPT. For tasks involving visual context, use Gemini.

Mistake 3: Missing Context

Problem: Assuming the AI knows your situation, audience, or constraints.

"Write a report about our Q3 performance" gives the AI nothing to work with. It doesn't know your industry, metrics, audience, or what Q3 performance entails.

Fix: Always include context: who's reading this, what decisions they're making, what they already know, what constraints matter.

Mistake 4: Vague Success Criteria

Problem: Asking for "good" or "better" output without defining what those mean.

"Make this email more professional" is subjective. Professional for a law firm differs from professional for a startup.

Fix: Define what success looks like: "More professional = formal greeting, third-person perspective, no contractions, evidence-based claims, no exclamation points."

For detailed analysis of common prompt mistakes and how to avoid them, see Avoiding Common AI Prompt Mistakes: Over-Constraining, Ambiguity & Context Assumptions.

Practical Implementation Guide

Start with existing tasks: Don't try to prompt everything at once. Identify your three most frequent text AI tasks and build templates for those.

Create a personal prompt library: Save prompts that work. Organize by category: business analysis, content creation, technical documentation, research, decision support.

Iterate systematically: When a prompt fails, use the scorecard to diagnose which component needs improvement. Don't randomly change everything.

Test across models: The same prompt may perform differently on ChatGPT vs Claude vs Gemini. Test important prompts on multiple models to find the best fit.

Build on patterns: The three foundational patterns (Structured Output, Chain-of-Thought, Multi-Perspective) handle most needs. Master these before creating custom patterns.

Document what works: Note which prompting techniques consistently produce good results for your specific use cases. Patterns emerge over time.

For advanced optimization strategies and systematic improvement techniques, see AI Prompt Iteration & Optimization: How to Get First-Attempt Quality Every Time.

Frequently Asked Questions

What's the difference between ChatGPT, Claude, and Gemini prompting?

Core prompting principles remain constant—all three need clear role, task, context, style, and constraints. Differences emerge in optimization: ChatGPT excels with explicit structure, Claude with reasoning steps, Gemini with multimodal integration. Choose model based on task requirements, not arbitrary preference.

Can I use the same prompt across all three models?

Usually yes with minor adjustments. Well-constructed prompts transfer effectively. ChatGPT may need more explicit formatting. Claude benefits from thinking-step instructions. Gemini works better with current information context. Test important prompts across models to identify optimal match.

How do I know which prompt pattern to use?

Match pattern to task type. Structured Output for consistent format needs (reports, documentation). Chain-of-Thought for complex analysis and problem-solving. Multi-Perspective for subjective decisions and creative exploration. Most tasks clearly fall into one category.

Why do templates sometimes produce poor results?

Templates are starting points requiring customization. Poor results typically mean missing context specific to your situation. Generic templates can't know your audience, constraints, or success criteria. Always adapt templates to your specific needs before use.

How can I improve prompt quality systematically?

Use the evaluation scorecard to diagnose failures. Identify which component (role, task, context, style, constraints) caused the issue. Modify only that component and retest. This systematic approach prevents random changes that waste time.

Should I use different prompting styles for ChatGPT vs Claude?

Subtle differences help optimization but aren't required. ChatGPT responds well to explicit structure and formatting instructions. Claude excels when prompted to think step-by-step and consider nuance. Both work with well-constructed prompts following core principles.

What makes a text AI prompt effective?

Completeness across five components: clear role (expertise to apply), specific task (output format), sufficient context (background and constraints), directed style (tone and voice), and defined constraints (requirements and limits). Effective prompts minimize ambiguity AI must resolve through assumption.

How long should prompts be for best results?

Length matters less than completeness. Five clear sentences covering all five components outperform five vague paragraphs. Optimal prompts are as long as necessary to eliminate ambiguity, no longer. Add detail where AI needs guidance, omit unnecessary elaboration.

Related Reading

Foundation:

• The Prompt Anatomy Framework: Why 90% of AI Prompts Fail Across ChatGPT, Midjourney & Sora - Core principles underlying all effective prompts

Visual & Video AI:

• Midjourney & DALL-E Image Prompts 2026: From Concept to Perfect Visual Output - Image generation prompt techniques

• Sora & VEO Video AI Prompts 2026: Cinematic Storytelling Made Simple - Video AI prompt strategies

Optimization & Troubleshooting:

• AI Prompt Evaluation Checklist: Diagnose Why Your Prompts Fail & Fix Them Fast - Systematic diagnostic framework

• AI Prompt Iteration & Optimization: How to Get First-Attempt Quality Every Time - Refinement strategies

Advanced Techniques:

• Role & Context in AI Prompts: Unlocking Expert-Level Outputs in Text, Image & Video AI - Deep dive on role framing and context loading

• Style & Tone for AI Prompts: How to Communicate Like a Human Across ChatGPT, Midjourney & Sora - Mastering voice and aesthetic direction

• Cross-Platform AI Prompting 2026: Text, Image & Video Unified Framework - Translating prompts across modalities

Common Pitfalls:

• Avoiding Common AI Prompt Mistakes: Over-Constraining, Ambiguity & Context Assumptions - Prevention guide

Ready-to-Use Resources:

• AI Prompt Templates Library 2026: Ready-to-Use Prompts for ChatGPT, Claude, Midjourney & Sora - Pre-built prompts

www.topfreeprompts.com

Access 80,000+ professionally engineered prompts for ChatGPT, Claude, Gemini, and all major AI tools. Every prompt built with the five-component framework for consistent, high-quality results across text, image, and video generation.