Five things worth generating this weekend

Pick something you can finish

The fastest way to understand a generation workspace is to build something small enough to finish in an afternoon and specific enough that you will notice when it is wrong. Broad exploration teaches you very little; a project with a definition of done teaches you where the sharp edges are.

Here are five we keep recommending. All of them fit in a weekend, none of them need a team, and each one exercises a different part of the stack.

1. A cover generator for your own writing

Take whatever you have already published — a newsletter, a blog, a folder of half-finished drafts — and generate a cover for each piece from its first paragraph. You will learn more about prompt structure in two hours of this than in a week of tutorials, because you have an opinion about every result.

The interesting part is not the first image. It is the tenth, when you start noticing that your prompts have a house style and that the style is doing more work than any individual instruction.

const covers = await alcove.images.generateMany(
  posts.map((post) => ({
    prompt: `Editorial cover art. Subject: ${post.summary}. Flat vector,
             two accent colours, generous negative space, no text.`,
    size: '1600x900',
  })),
  { family: 'prism', concurrency: 4 },
);

2. A summariser that has to fit on an index card

Constraint is the whole exercise. Build something that reduces any input to under forty words, then feed it things that resist summarising: a legal notice, a recipe, an argument between two people in a support thread.

You will discover very quickly that the model is not the hard part. The hard part is deciding what “the point” of a document is, which is a question the tooling cannot answer for you.

3. A chatbot with exactly one job

Not an assistant. One job. A bot that only answers questions about your opening hours, or only converts units, or only tells you whether a given file format is supported.

Narrow bots are easy to evaluate, which means you can actually tell whether a prompt change made things better. Broad assistants are impossible to evaluate, which is why so many of them ship half-finished and stay that way.

4. A pipeline that regenerates itself nightly

Wire a scheduled job that regenerates a small set of assets every night and writes the results somewhere you will see them. A dashboard header, a set of social cards, the illustration on your own landing page.

Two things happen. First, you find out how much variance there is between runs of the same prompt — usually more than you expected. Second, you stop treating generated assets as precious one-offs and start treating them as output, which is a much healthier relationship with the tooling.

5. A model bake-off with a real scoring rubric

Take one task you actually care about, write twenty representative inputs, and run them through three families. Score the outputs against a rubric you wrote before you looked at the results.

The rubric is the point. Without it you will pick the family whose failures happen to be the most charming, and you will be very confident about it. With it you will usually be surprised, and occasionally you will find that the cheapest option is entirely good enough.

The common thread

Every one of these projects has a clear finish line and a way to be wrong. That combination is what turns tooling from something you have read about into something you actually know. Pick one, give it a Saturday, and throw it away on Sunday if it was not interesting. The knowledge stays.