Prompting Across Different AI Tools
The principles of prompt engineering are universal — clarity, specificity, context, and structure improve results across all AI models. But each major AI tool has its own characteristics, strengths, and behavioral tendencies. Understanding the differences between tools helps in choosing the right one for the task and in adapting prompts to get the best results from each.
The Major AI Language Models
As of 2026, the most widely used AI language models for general prompting are:
- ChatGPT (OpenAI) — GPT-4o and later versions
- Claude (Anthropic) — Claude 3 and Claude 4 series
- Gemini (Google) — Gemini 1.5 Pro and later versions
- Copilot (Microsoft) — powered by OpenAI models, integrated into Microsoft products
- Llama (Meta) — open-source model widely used in self-hosted and custom deployments
All of these are capable, general-purpose language models. The differences lie in their default behaviors, training emphasis, content policies, context handling, and integration strengths.
Key Differences Between Major AI Tools
ChatGPT (OpenAI)
Strengths:
- Extremely versatile — strong across writing, coding, analysis, and conversation
- Strong code generation and debugging capabilities (especially with GPT-4o)
- Wide plugin and tool ecosystem for extended functionality
- DALL-E integration for image generation in the same interface
Prompting notes:
- Responds well to detailed, structured prompts
- Custom GPTs allow building tailored tools with pre-set system prompts
- For coding tasks, specifying the language, version, and libraries is important
Best for: Code generation, technical tasks, multi-tool workflows, image generation alongside text
Claude (Anthropic)
Strengths:
- Exceptionally strong at long-form writing, nuanced analysis, and reasoning
- Large context window — can process very long documents in a single conversation
- Known for following nuanced, multi-part instructions carefully
- Strong at maintaining consistent tone and style across long content
- Thoughtful handling of complex or sensitive topics
Prompting notes:
- Responds particularly well to detailed context and clearly structured prompts
- Excellent for document review, long-form content, and nuanced analysis tasks
- Will often push back or ask for clarification when a prompt is ambiguous — treat this as a feature, not a flaw
Best for: Long documents, detailed analysis, writing with nuanced tone requirements, complex reasoning
Gemini (Google)
Strengths:
- Deep integration with Google Workspace (Docs, Sheets, Gmail, Drive)
- Strong multimodal capabilities — processes images, audio, video, and text
- Access to real-time Google Search integration for current information
- Strong performance on tasks involving structured data and research
Prompting notes:
- When using within Google Workspace, prompts can reference documents and files directly
- Specify clearly whether real-time web search should or should not be used
- For multimodal tasks, prompts that specify exactly what aspect of an image to analyze work best
Best for: Google Workspace workflows, research with live web data, multimodal tasks, data analysis in Sheets
Microsoft Copilot
Strengths:
- Deep integration with Microsoft 365 (Word, Excel, Outlook, Teams, PowerPoint)
- Can reference and act on files within the Microsoft 365 ecosystem
- Strong for business productivity workflows
- Bing search integration for current information
Prompting notes:
- Prompts in Copilot can reference existing documents: "Based on the proposal I have open in Word..."
- Copilot in Excel responds well to natural language data requests: "Create a pivot table showing sales by region and quarter"
- Explicit instructions about format work well (e.g., "format this as a Word heading style")
Best for: Microsoft 365 users, business document automation, Outlook email drafting, Excel data tasks
Adapting the Same Prompt for Different Tools
A well-structured prompt will generally work across all these tools. However, small adaptations can squeeze more quality from each:
| Task | ChatGPT Adaptation | Claude Adaptation | Gemini Adaptation |
|---|---|---|---|
| Summarize a long document | Paste document; specify word count | Use the full document in context; specify structure | Share via Google Drive link if in Workspace |
| Generate code | Specify language, version, libraries clearly | Add detailed requirements and error handling instructions | Works well; specify language explicitly |
| Research current events | Enable browsing if available; or note knowledge cutoff | Use Claude with web search enabled; note knowledge limits | Real-time search is a strength — use it |
| Draft a business email | Works well with standard structured prompt | Specify tone carefully — Claude follows nuanced instructions very well | In Gmail, Copilot can access email thread context |
Choosing the Right Tool for the Job
| Task Category | Recommended Tool |
|---|---|
| Complex code generation and debugging | ChatGPT (GPT-4o) |
| Long-form writing, detailed analysis | Claude |
| Research with real-time web data | Gemini or Copilot |
| Microsoft 365 document workflows | Microsoft Copilot |
| Google Workspace productivity | Gemini |
| Multimodal tasks (image + text) | ChatGPT, Claude, or Gemini (all support this) |
| Custom AI tools and agents | ChatGPT Custom GPTs or API-based Claude/GPT |
Model Behavior Differences to Be Aware Of
Response Length Tendencies
Different models have different defaults for response length without explicit instructions. Claude tends toward thorough, detailed responses. GPT-4o can be similarly verbose or concise depending on how the prompt is framed. Always specify length when it matters — do not rely on default behavior.
Safety and Refusal Behavior
All major AI tools have content policies, but they apply them differently. One model may produce content that another refuses, or vice versa. This variation is not a reason to use one tool over another for circumvention — the ethical principles from the previous topic apply regardless of which tool is used.
Context Window Size
Context window refers to how much text the AI can process in a single interaction. Claude has one of the largest context windows — making it particularly strong for tasks involving long documents. When working with very long content, choosing a model with a large context window prevents the AI from losing earlier information.
Testing Prompts Across Multiple Tools
For high-stakes prompts — especially those used in products or workflows — it is worth testing the same prompt across two or three tools to compare output quality. Sometimes a prompt that underperforms on one model works very well on another. This cross-tool testing is a lightweight but powerful quality step.
Key Takeaway
The core principles of prompt engineering apply universally. But each major AI tool — ChatGPT, Claude, Gemini, Copilot — has distinct strengths that make it better suited for certain types of tasks. Understanding these differences allows for deliberate tool selection and small prompt adaptations that get the most out of each model. No single tool is best for everything — knowing when to use which tool is itself a valuable skill.
In the final topic of this course, we will cover Building a Personal Prompt Library and Workflow — how to organize, maintain, and continuously improve a collection of prompts for maximum long-term productivity.