MODULE 1.1
Models: choose by task
Compare models with a real task and a quality criterion.
What it is
When this course talks about AI, it means language models, the LLMs. A model is a mechanism trained to produce text from what you give it. There are several, with different names, sizes and costs, and the first question is usually which one is best. That question has no useful answer: there is no best model, there is the model suited to the task, the deadline, the cost and the level of checking that work requires. That is why this module compares models against a real task rather than against opinions. A model produces answers; a system organises how those answers become work. OSWork combines model, interface, files, instructions, tools, memory and automations. Think of a small workshop: the professional's skill matters, but tools, materials and quality criteria also determine the result. We are not installing a new computer operating system: we use that expression as a metaphor for organisation.
Why learn
Without this distinction, every failure turns into an attempt to write a larger prompt. Sometimes only the input file, a permission, or the way to verify the output is missing. Separating the pieces allows you to fix the right point.
Key concepts
Model reasons; interface receives the goal; files provide evidence; tools execute; you verify.
What it is
Before comparing names, sort by function. Language models, the LLMs, write, summarise, explain and program: that is where families like GPT sit, with Sol, Terra and Luna, GPT-6 Astra, and the Claude family, with Opus and Fable. Image and video models generate or edit visual material. And there are classification models, such as Jev, which do not converse: they take alternatives and return a choice, a yes or no, or a score. There are also hubs, which give access to several providers through a single point: OpenRouter for language models and Kie for image and video. Names, versions and availability change; this reading is from 20/09/2026.
Why learn
Adopting one model as the best closes the door on everything that model does not do. An excellent LLM will not generate a video, and a classifier will not write your report. Understanding what each kind is for comes before choosing, and the choice happens when the task appears, and may be different the following week. Availability also varies by account, client, authentication and rollout.
Key concepts
Text; image; video; classification; access hubs; availability.
What it is
The model is the chosen mechanism. Reasoning effort is a setting of that mechanism. Higher levels may consume more time and tokens, units of text processing. The selector names change between Chat, Work, and Codex; there is no single list of Instant, Medium, High, and Pro that represents all products.
Why learn
Requesting the maximum on every task can increase consumption without improving the result. Increasing effort also does not provide a missing document. First complete the inputs, then assess whether the problem requires more analysis.
Key concepts
Model and effort are different axes; start with the default; compare under equal conditions.
What it is
A rubric turns opinion into observation. Define before the request which facts must appear, which errors are unacceptable, and how the output will be used. Use three small criteria: fidelity to the data, combined format, and verifiability of the conclusions.
Why learn
Without criteria, you choose the prettiest answer. A report can sound convincing and alter values. The test should reflect the work you need to deliver, not a demonstration made to impress.
Key concepts
Acceptance; evidence; representative sample; controlled comparison.
What it is
Signing in with ChatGPT uses the rights and limits tied to the account and the workspace. An API key uses pay-per-use billing on the platform. Subscribing to ChatGPT does not mean receiving free balance for any program that calls the API. Hubs such as OpenRouter and Kie have their own credit, billed per use and separate from any subscription. Check the active method before a long run.
Why learn
This precaution avoids discovering later that an experiment is consuming a different account. Usage limits, model access, and data rules can change; the source of truth is the current configuration of your account.
Key concepts
Subscription; authentication; API; consumption; spending limit.
What it is
Autonomy is an authorization with scope, not an invitation to do anything. Combine objective, permitted files, authorized actions, time limit, and stop condition. The output must include what was done and how it was verified.
Why learn
An agent can execute more steps than a chat, including making multiple mistakes. A simple limit protects time, budget, and files without preventing useful work. Technical permission and written instruction complement each other.
Key concepts
Scope; approval of external actions; execution ceiling; reviewable result.