Two websites both say they use AI. One answers questions about an uploaded handbook. The other generates text from a blank chat. They might even use the same underlying model. Why do they behave differently?
Before you begin: Know the difference between a model and a learning task.
Separate four layers
The model is the learned computation that produces outputs from inputs. The runtime or provider makes that model available for use. The application adds instructions, data, tools, storage, and an interface. The workflow is how a person or organization uses that application to complete a job.
For a workshop help desk, the model may generate a reply. The application retrieves the current timetable and limits which documents are available. The workflow requires an organizer to approve changes to registration. If the reply contains last month's timetable, the problem might be stale retrieval data rather than the model's general language ability.
This lesson replaces an older year-based product overview. The URL remains stable so existing bookmarks keep working. The useful skill is learning to inspect a system, not memorizing which product was fashionable in one year.
Name the input and output
A text model accepts and produces text representations. A multimodal model can work with more than one kind of input or output, such as images, audio, or text. “Multimodal” does not tell you which combinations a specific product supports. A product that reads an image may not generate one, and a model that handles audio may be exposed through an application that accepts only text.
An assistant can also call a separate tool. If it searches the web, runs code, or reads a file, those actions may involve systems outside the language model. Ask what information the tool returned and how the application used it. The model's answer and the tool's evidence are related but distinct.
Compare access choices
With a hosted API, you send requests to a service. The provider operates the model infrastructure, while you remain responsible for the data you send and your application's behavior. With a model you run yourself, you manage the hardware and runtime. You still need to inspect its license, requirements, and limitations.
“Open weights” means model parameters are available under specified terms. It does not by itself mean unrestricted use, a complete public training dataset, or an entirely reproducible training process. “Local” means computation happens on a chosen machine; it does not prove that the whole application is offline or that its logs stay private.
Work through a product decision
You need a helper for public workshop questions. It must quote the current cancellation policy and refuse to guess when the policy is missing. Start with three checks: can you provide the policy, can the answer show where its statement came from, and can you test a missing-answer case?
Next, check cost, response time, accessibility, and data handling. A model with a larger benchmark score may still be a worse product choice if the application cannot attach the right evidence. A small retrieval-and-answer system may fit the job better than an autonomous agent with broad tools.
Write down the exact product, model identifier if exposed, access plan, and review date when you compare options. Availability, limits, and terms can change independently. This course avoids presenting a permanent winner where the answer depends on the task and account.
Investigate a claim
A tool advertises “private AI with the latest knowledge.” What two questions would you ask before uploading a document?
Turn the claim into checkable questions
For privacy, ask where processing occurs, what is retained, who can access it, and whether your account's controls meet your needs. For current knowledge, ask whether answers use live retrieval, how source dates are exposed, and what happens when retrieval fails. A broad slogan does not answer either question. You can test freshness with a public document that changed recently without first uploading private material.
The next lesson looks inside the language model itself. Keep this layered map in mind: knowing how a model works will not explain every behavior of an application built around it.
Further reading
The original Model Cards for Model Reporting proposes documenting intended uses, evaluations, and limitations. The Hugging Face model-card documentation shows how these records are represented alongside models.