Model routing — switching between Claude, GPT, Gemini mid-task
Operators don't pick one AI. They route each task to the model that does it best. Knowing the strengths is the skill.
::TL;DR · the whole lesson in three lines
- MOVEOperators don't pick one AI. They route each task to the model that does it best. Knowing the strengths is the skill.
- DRILLTake three real tasks from this week. Route each to a different AI. Notice the difference.
- WINYou have a working routing intuition.
::concept · what's actually happening
Claude (Anthropic) is best at: longform writing, careful reasoning, code review, anything where being precise and thoughtful matters more than being fast. Long context window. Strong refusal posture (which is a feature for some tasks, a friction for others).
read full concept · 4 more paragraphs →collapse concept ↑
GPT (OpenAI) is best at: general versatility, image generation (DALL-E), image input + understanding, tooling and plugins, anything where you want the broadest set of features in one app.
Gemini (Google) is best at: anything tied to Google Workspace (Docs, Gmail, Calendar), live web search (it has Google), enormous context windows (1M+ tokens for Gemini 1.5+).
Perplexity is best at: fact-bound research where you need source citations. Treat it as a search-with-AI tool, not a general chat.
The skill isn't memorizing this. The skill is asking yourself "which model fits THIS task" before you pick the tab.
::drill · do the thing
Take three real tasks from this week. Route each to a different AI. Notice the difference.
::L11 drill · copy-paste into any AI chat
(Three tasks, three AIs. Same prompt template; different windows.) Pick three tasks you have to do this week. They should be different in nature: 1. A writing task (email, draft, summary) → Claude 2. A research / fact task → Perplexity (or GPT with web on) 3. A Google Docs / Gmail / Calendar task → Gemini For each: run the same prompt template from Lesson 2 (context / constraint / output). Note which AI felt right for that task.
::steps
- 01Identify three tasks across three categories.
- 02Run each on the matching AI.
- 03Note (in your prompt library) which AI you'd use for that category next time.
- 04Don't overthink — calibration improves over months, not days.
::outcome · what should be true
- You have a working routing intuition.
- You stop defaulting to one tool for all tasks.
- You feel the limit of any single tool when paired with the wrong task.
::trap · the most common failure
Subscribing to all three at $20/mo each because you want to route. You can route between free tiers. Pay for the ONE you use most. Use free tiers for the others.
::other lessons at Operator level
Local AI · Ollama — privacy, offline, and the limit of free
At Operator level you need an honest opinion about local-only AI. Even if you don't use it daily, you should have run it once.
MCP servers — the plug socket that turned AI into a real tool
Model Context Protocol is the standard plug. Knowing what plugs in changes what your AI can actually touch — your files, your inbox, your calendar, your repos.
Agent mode — when AI takes action, not just answers
The frontier of useful AI is agents that DO things — browse, click, file, send. The actual skill is the safety pattern, not the magic.
Computer use — when AI takes the mouse and keyboard
Claude in Chrome, ChatGPT Atlas, computer-use beta — the frontier is AI that drives your browser like a human. Knowing the safety pattern is the actual skill.
What AI cannot replace — taste, judgment, relationships
The operators winning in 2026 are the ones who learned what AI is for and what is theirs. Knowing the line is more valuable than any prompt.
Agents 101: model plus tools plus loop
An agent is a model with tools running in a loop until done · know when you need one and when you don't.
MCP: structured tools for AI
Model Context Protocol is the USB-C of AI tooling · learn the shape before you wire anything.
Skill primers: teach a session your context in 30 seconds
A skill is a reusable file that primes a fresh AI session with your project, voice, and rules · stop re-explaining yourself.
Local models with Ollama
Run Llama, Qwen, or Mistral on your own laptop · no API, no logs, no monthly bill for the work that should stay home.
Vision models: when to use them
Vision lets the model see images · powerful for screenshots and diagrams · weak for precise spatial work · know the line.
Audio and Whisper transcription
Whisper turns audio into text · meetings, voice memos, interviews · the AI-era replacement for note-taking.
RAG vs long context: when to retrieve, when to dump
RAG fetches the right slice of your data at query time · long context stuffs everything in · know which problem you actually have.
Embeddings: meaning as numbers
An embedding is a list of numbers that captures the meaning of text · learn the shape and you unlock semantic search, deduplication, and clustering.
Fine-tuning vs prompt engineering
For individuals, fine-tuning is almost never worth it · know exactly when it actually is.
AI safety in personal use
PII, NDAs, financial data, and other people's secrets · know the rules of what you do not paste.
Multimodal prompting: combining text, image, audio
The strongest prompts use the medium that fits the question · sometimes you describe, sometimes you show, sometimes you do both.
Chain-of-thought: making the model show its work
Asking the model to reason step-by-step before answering raises accuracy on hard problems · know when it earns its cost.
Tool use and structured output
Function calling makes the model return JSON your code can use · know the contract before you build on it.
Cost optimization: tokens, caching, model selection
AI is metered · the operators who stay profitable measure what they spend and choose the model that fits the task.
::part of the AtomEons /learn curriculum · 45 lessons · 5 levels · cc-by 4.0