Most people use the wrong AI for the wrong task.
🤓 Above is the ultimate cheat sheet !
1 - Search (Web Browsing)
Finds real-time info with sources.
☑︎ Best AI: Perplexity
Prompt: "Find latest UK inflation figures."
2 - Deep Research
Multi-step synthesis with citations.
☑︎ Best AI: Gemini 2.5
Prompt: "Analyse top trends in B2B SaaS marketing for 2025."
3 - Vision – Image Input & Generation
Upload and analyse or generate images.
☑︎ Best AI: ChatGPT-5
Prompt: "Analyse this chart for trends."
4 - Vision – Video Input & Generation
Upload, analyse, or generate video content.
☑︎ Best AI: Gemini 2.5
Prompt: "Summarise key scenes in this marketing video."
5 - Camera Mode
Guides you live via device camera.
☑︎ Best AI: Google AI Studio
Prompt: "Help me fix this Excel formula."
6 - Voice Mode
Hands-free voice conversation.
☑︎ Best AI: ChatGPT-5
Prompt: "Summarise the news while I drive."
7 - File Uploads
Summarises, answers questions on docs.
☑︎ Best AI: Claude Opus 4.1
Prompt: "Summarise this 80-page strategy deck."
8 - Data Analysis (Code Interpreter)
Cleans, analyses, and visualises data.
☑︎ Best AI: Claude Opus 4.1
Prompt: "Plot sales from this CSV."
9 - Canvas (Collaborative Workspace)
Live co-writing or coding.
☑︎ Best AI: Claude Opus 4.1
Prompt: "Create a landing page mock-up."
10 - Memory (opt-in)
Remembers facts you set.
☑︎ Best AI: ChatGPT-5
Prompt: "Remember my preferred writing style."
11 - Custom Instructions
Sets tone and style for all chats.
☑︎ Best AI: ChatGPT-5
Prompt: "Always use concise bullet points."
12 - Projects
Groups related chats & files.
☑︎ Best AI: Claude Opus 4.1
Prompt: "Organise all Bootcamp launch materials."
13 - Scheduled Tasks (Automations)
Sends reminders or runs actions.
☑︎ Best AI: Perplexity
Prompt: "Remind me every Monday at 8am."
14 - Custom GPTs
Builds niche AI assistants.
☑︎ Best AI: ChatGPT-5
Prompt: "Create a GPT that writes sales emails."
15 - Agent Mode
Executes tasks from research to booking.
☑︎ Best AI: Perplexity
Prompt: "Plan my Tokyo trip with itinerary."
16 - Connectors
Links your LLM to external apps.
☑︎ Best AI: Claude Opus 4.1
Prompt: "Connect to my CRM and summarise latest sales pipeline."
17 - Study and Learn Mode
Guides you through lessons, quizzes, and deep understanding.
☑︎ Best AI: Gemini 2.5
Prompt: "Teach me intermediate Python with exercises and feedback."
Many expect an AI bubble.
Fewer realize there are already contenders for what comes next.
The LLM backlash is getting louder. And the question behind it is simple:
"did we build an AI bubble on the wrong foundation"?
The magic of LLMs is undeniable. But so are the cracks.
- Hallucinations delivered with confidence.
- Reasoning that buckles as soon as you push it.
- Training costs that stretch into the billions.
- Models built on data scraped from the unfiltered chaos of the internet.
The hype cycle is starting to turn.
Investors, founders, and researchers are quietly asking the same thing:
If this is the peak of the LLM wave, what comes after it?
Three emerging approaches offer clues.
1. World models — AI that predicts reality, not just text
World models do not guess the next word.
- They simulate the next moment.
- They build internal models of objects, motion, and consequences.
- They reduce hallucinations because they follow the logic of the world, not the logic of language.
This is why they are becoming the backbone of robotics and agentic systems.
2. Causal AI — AI that understands cause and effect
While LLMs learn surface patterns, causal AI learns why things happen.
It can answer:
- What caused an outcome.
- What to change.
- What action will work next.
It generalizes better, breaks less under distribution shifts, and explains its reasoning.
This makes it powerful for decisions where mistakes are expensive.
3. System 1 / System 2 architectures — intuition plus deliberate reasoning
LLMs today behave like System 1: fast, instinctive, improvisational.
The next step is adding System 2:
- Slow, structured reasoning layered on top.
- Draft, then verify.
- Idea, then logic.
- Speed, then discipline.
It is early research, but it could dramatically reduce errors without losing creativity.
LLMs will not vanish.
But the next decade will not be defined by scaling language models endlessly.
It will be defined by architectures that can reason, predict, and explain.
