Model the use case first

Once you have an objective, the next step is to model the use case the way you would model an application before writing code. Local AI projects fail for the same reason software projects fail: nobody drew the shape of the thing first.

What to model

The data. What documents, records or inputs exist? Where do they live? What is sensitive and what is not? How much is there, and how fast does it change?
The flow. What happens to an input from start to finish? Who triggers it, what does the model do, where does the output go?
The constraints. Privacy rules, latency budget, cost ceiling, who may see what.
The schema. What are the real entities? If you are working with clients, cases and documents, write those down as structured shapes, even before any code.

Why schema before model

If you can describe your problem as a schema, you have already done half the design. "A client has many cases; a case has many documents; a document has a type and a status" is a model. A model with a schema is ten times easier to feed to an LLM than a pile of text, because the LLM can work on structured fields instead of guessing.

The local-vs-cloud angle

Modelling the use case forces the privacy decision into the open. Once you can see which fields are confidential, "can this go to the cloud?" is a concrete per-field question, not a vague feeling. That feeds directly into the next lesson.

The output

A one-page design: the objective, the data, the flow, the constraints, and a rough schema. You can draw it, write it, or both. The point is that it exists before you pick a model or a runtime.

This is the same discipline as designing a web app: think in terms of use cases and data, not tools and features. The LLM is just one component in that design, and it is a lot easier to choose the component once the design exists.