Give (topic)

Give, in AI development, is the practice of supplying context, examples, and direction before asking a model to produce output. The transcripts frame it as one side of a two-sided exchange: what you give to the AI and what you demand back from it. On the give side, you provide an audience to write toward, the substance the model can shape, and your voice so it writes in your style. Without that, you get what one video calls "average writing" and generic output.

Why does it matter? Because AI's sole purpose, as described in multiple transcripts, is to make you happy by giving you answers. If it doesn't know something, it fills in the blank. Adjusting its incentives — telling it it's okay not to give you an answer — requires you to first supply enough context that it can recognize what it doesn't know. The Claude Code course puts it bluntly: without context, AI is "just a smart intern who's guessing." The context you provide is mostly private data — your expertise, your brain, your intellectual property — and that is what makes outputs unique. If everyone asks the same model the same empty question, everyone gets the same average result.

Give also shapes what the model generalizes. In the skills discussion, one topic serves as a sample of how a process should be done, and the model must abstract that for future tasks. Concrete give includes setting a goal up front before rambling context, and feeding a database or API so the agent can find good examples when it needs them.

To build with Give, start by understanding what you give to the AI and what you demand back. Give is about directing work, not just asking for output. For example, when naming your reader rather than your topic in a prompt, you give the model context that shapes everything. This works especially well with cutting-edge models like Fable 5, Opus 5, and GPT 5.6.

Next, give the model the right data. MCP (Model Context Protocol, a way to connect AI to tools and data) acts like a toolbox with all the different tools available. With MCP, you use your words to request concepts, and the system generates options. MCP apps (interfaces that let models see and use data inside chat) give the model data and give the user a UI (user interface, the visual elements you interact with). When you click within an MCP app, it can send a prompt back to the model, like asking it to explain a specific step.

Then, give the hardest tasks to the best model. Use Fable for planning, for example. Give it your hardest problem and ask for a full spec (detailed specification, a written plan). It reviews your codebase and thinks through how to build the feature.

You can also give a budget to stay under, so the tool doesn't blow through your limits. To start building, run the command NPX create MCP app, which gives you a template you can serve. You can also register a tool by giving it a name, a description, an input schema (a structure defining what inputs are allowed), and a callback. This mirrors how MCP works.

Finally, give examples when output requires judgment or style. Wrap context in XML tags (markup labels that separate sections) so the model knows what material to use. Let the evaluation decide whether extra examples earn their space. This habit becomes a prompt contract, a reusable structure for giving clear tasks.

When building MCP apps (tools that connect models to data and interfaces), the first rule is simple: anything you show the user must also be given to the model as data. A common mistake is injecting HTML onto the page so the user sees a nice interface, while the model has no idea what data is being displayed. If you ask it to rank companies or explain a result, it cannot help — it never saw the content. So design every UI element with a matching data payload.

Another pitfall is treating prompts as magic. Bad prompts like "build me a website for my dog walking business" produce generic output; good prompts name the business, the page type, and the sections. Give the model the cleanest, tightest version of what you want, not a "junk drawer" of loose context — otherwise you hit context rot (degraded output from cluttered input). One good example often beats twenty minutes of wordsmithing the perfect prompt.

For model choice, give your hardest problem to the best model — for example, use it for full spec planning across your codebase. And when using MCP servers (interfaces exposing documentation or tools), remember they only help if the model can reason over what they return. Models don't learn continuously like humans, so they cannot absorb undocumented code or missing context on their own.