Every model has a personality, a sweet spot, and a list of things it secretly hates. The fastest way to upgrade your AI workflow is not learning a new prompting trick — it is matching the model to the task. This tutorial helps you stop forcing one model to do everything.
Note: Model capabilities evolve every few months. Treat this guide as a stable mental model, but always sanity-check the latest releases against your specific workload.
Once you've spent a few weeks across the major tools, a pattern emerges. ChatGPT feels like a quick generalist. Claude feels like a thoughtful editor. Gemini feels like a research-and-Workspace assistant. Perplexity is a citing librarian. Midjourney is a stylish artist; Stable Diffusion is a controllable studio. Each has work it loves and work it merely tolerates. Using the right one for each job is more important than any single prompt technique.
This tutorial gives you a decision-friendly comparison across the dimensions that actually matter — reasoning depth, document length, factuality, multimodality, integration, cost, and personality — then maps real tasks to the model that handles them best.
Forget benchmark scores; they shift with every release. Compare models on the dimensions you actually feel during real work:
The fastest way to internalise the differences is by task. Here is a working playbook:
The highest-leverage workflows often use two or three models in sequence. Each plays to its strength.
A two-model research-and-write pipeline
1. Perplexity (research)
"Summarise current EU AI Act obligations for SMEs in 2026.
Cite sources."
2. Claude (synthesis)
Paste the Perplexity output and its citations.
"Turn this into a 1,200-word explainer for a non-technical
audience. Keep all citations as footnotes. Use UK English."
3. ChatGPT (packaging)
"Take the explainer below and produce 5 headline options,
a 155-character meta description, and 3 LinkedIn post variants."Each model handles the part it is best at. The total quality is higher than any one of them on its own — and it doesn't cost much more time.
Pick one real task on your desk and run it through three different models. Score each on accuracy, voice, and time-to-good-draft. Note which model wins for this type of task, not just this instance.
Build a two-model pipeline for something you do repeatedly — research-and-write, brainstorm-and-edit, or read-and-summarise. Document the prompt and model used at each step. Save it as your default workflow.
Take the task-to-model cheat sheet above and replace one item with your own. Maybe ChatGPT wins where I said Claude does — for your work. Calibrate the playbook to your actual usage patterns.
Sign in to join the discussion and post comments.
Sign inAdvanced Prompt Engineering Techniques
Master the powerful techniques AI experts use every day. Chain-of-thought, RAG, agents, function calling, prompt evaluation, and much more — 20 deep-dive tutorials.
Foundations of Prompt Engineering
The must-know basics of prompt engineering. Learn what prompts are, how AI models read them, and how to write clear instructions that get great results.
Prompt Engineering for Data Science & Analytics
Supercharge your data workflows with AI. 15 practical tutorials on using prompt engineering for data cleaning, EDA, machine learning, SQL, visualisation, and more.
Prompt Engineering for Developers
Use AI as your coding co-pilot. 18 tutorials on writing prompts to generate clean code, debug faster, write tests, build APIs, and ship better software.
Prompt Engineering for Business & Productivity
Use AI to work smarter — automate tasks, make better decisions, and communicate professionally. 12 practical business prompt tutorials for professionals.
Prompt Engineering Projects & Real-World Applications
Twelve hands-on projects that turn prompt engineering theory into a portfolio. Build chatbots, content generators, RAG systems, and more.