Choose Your Collaborator.
Artificial intelligence is not a single, omniscient brain. It's an ecosystem of specialised workers. Some are brilliant artists who can't do basic maths. Others are flawless accountants who can't write a convincing poem. Choosing the wrong worker guarantees a flawed result. This class teaches you to audit any model — by memory, logic and privacy — and assemble the right toolkit.
Tell models apart
Distinguish proprietary from open-source, and match each to a task.
Define context windows
Explain the memory limit that makes models forget — and hallucinate.
Protect your data
Spot the privacy implications of feeding info into corporate systems.
Match the task to the model
Drag each task into the model best suited to it. Keyboard user? Use the "Send to…" menu on each card.
The Analytical Model
The Creative Model
The context window
The single most important spec isn't how smart a model is — it's how much it can remember. That memory limit is the context window, measured in tokens (the word-fragments from Class 1).
You have short-term memory: you recall what was said five minutes ago. A model's short-term memory is strictly capped by its context window. With an 8,000-token window (~6,000 words) it can hold the first chapter of a book. Paste in a 50,000-word novel, though, and it suffers instant amnesia — operating first-in, first-out, it forgets the beginning by the time it reaches the end.
If a user summarises a contract that exceeds the context window, what happens when the AI forgets the first half? It confidently invents clauses that don't exist to fill the gap. This is a primary cause of AI failure at work.
Context windows range from 8,000 to over 1,000,000 tokens. Picking a model whose memory can hold your whole document is the operator's primary responsibility.
Two architectures, side by side
Big memory, locked data
Massive context window and high capability — but your inputs travel to a corporate server, the decision process is a black box, and cost is high.
Smaller memory, total privacy
Free to download and run locally — smaller context window, but the data never leaves your machine. You can literally pull the network cable and it still works.
The trade is always the same: raw, effortless power versus absolute privacy. Your job is to decide which the task demands.
The Capability Radar
Every model is a compromise. Fast models rarely do deep logic; highly creative models often fumble strict formatting. Move the sliders to define your project's needs and watch the radar find the closest model archetype.
No shape is "best." Max out Memory and Logic and you'll land on a corporate powerhouse; max Cost-efficiency and Speed and you'll be steered to a small local model. The right model is the one that fits your shape.
Walled garden vs mutual-aid library
The ecosystem splits into two camps. Understanding the difference is essential for data security, creative independence and ethical deployment.
The corporate walled garden
Models like ChatGPT, Claude or Gemini are owned, hosted and controlled by big tech, accessed via the cloud. Powerful, but black boxes: you can't see how they decide, and the company may analyse your inputs. For journalists, activists, medical and community workers, that lack of data sovereignty can be disqualifying.
The mutual-aid library
Labs release the weights and code for free (e.g. Llama, Mistral). Anyone with a capable computer can run them entirely offline. Sever your internet connection and the AI still works — mathematically provable privacy for vulnerable community data, client notes, or unpublished manuscripts.
Open-source models demand technical setup and serious local hardware (a strong GPU), and — because they must fit on a laptop — may have smaller context windows and lower capability. You're always weighing effortless power against absolute privacy.
Never default to a single corporate tool. Audit the task: public data needing massive logic → the walled garden. Highly sensitive private data → download an open-source model and sever the connection.
Sam & the archive
Sam leads a local queer-history archive and has 400 pages of scanned 1980s community newsletters to categorise by date, subject and people mentioned.
Wrong tool
Sam used a standard free model with an 8,000-token window. It read the first 10 pages, forgot them, and hallucinated fake historical names for the other 390 — the document vastly exceeded the memory limit.
Right tool
Sam audited the ecosystem, recognised this needed a huge memory buffer, and switched to a model with a 200,000-token context window. It ingested all 400 pages at once.
Knowing hardware limits stops you blaming yourself (or the prompt) when the real failure was a tool with too little memory. Because these archives were public historical data, a corporate model was fine. Had they held private medical records, Sam would be obligated to use a local open-source model, chunking the document to fit a smaller window.
Map current memory limits
Context-window limits shift constantly under fierce competition. Last year's "massive" buffer is today's standard. Open a search engine, find the current limits, and log them below (saved on your device only).
What to look up
"Claude maximum context window" · "Gemini Pro context limit" · "how many pages is 1 million tokens?". Find which commercially available model currently has the largest context window, and how many book pages it can hold.
The blind taste test
Three outputs were generated from the same prompt: "Write a polite rejection letter for a freelance design project." Read them, then map each to a model type — citing the linguistic markers that gave it away.
Rigid & formal
Uses bullet points and cold, highly formal corporate syntax throughout.
Warm & conversational
Empathetic, uses exclamation points, and offers creative alternatives.
Broken
Repeats the same sentence twice and abruptly cuts off at the end.
Reveal the answer mapping
Output C is the older / smaller model — it lost memory and hallucinated a repetition. Output A is the logical/analytical model — it defaulted to bullet points and cold syntax. Output B is the creative model — empathetic adjectives and conversational flow. The skill is citing specific markers (bullets vs exclamation marks vs degradation), not guessing.
Success rubric
Needs work: guesses with no analysis of markers.
Getting there: spots the broken model but confuses logical and creative.
Solid: maps all three but without citations.
Excellent: maps all three and explicitly cites the logical model's bullets vs the creative model's adjectives.
Quiz — 5 questions
Common mistakes
The loyalty trap
Sticking to one comfortable chatbot for everything — then using it to crunch a spreadsheet and getting maths errors. Fix: keep three models bookmarked and swap by task.
The endless paste
Dumping 50 pages into a 10-page window, so the AI hallucinates the ending. Fix: estimate token limits first; break large text into chapters and process sequentially.
The free-privacy assumption
Pasting proprietary strategy or unreleased work into a free public bot. Fix: treat free models like a public billboard — if you wouldn't tape it to a window in a public square, don't paste it.
Ignoring "temperature"
Leaving defaults and getting wild code or stiff poetry. Fix: temperature controls creativity — set it to 0 for logic and maths, 0.8+ for brainstorming.
Whose worldview wins?
If a single corporation builds the most dominant AI in the world, how might relying on that one model's built-in tone and logic constraints enforce a standardised, homogenised worldview on digital culture?
Optional prompts: How does building independent, open-source models empower marginalised communities to protect their data? Would you sacrifice the convenience of a fast corporate AI to keep your data fully private on a local machine? Does the "walled garden" limit digital creativity?
You can now audit and select the exact right tool for any task — no longer bound to a single interface. Next, we learn to command these models: from simple questions to engineered, structured prompts.