Put AI inside your product
Ship AI features users actually feel, not a bolted-on chatbot.
3 steps to get you moving, each with a resource worth your time and more waiting underneath
Think of this as a friendly starting line, not the last word. Each step gives you the gist, then a resource worth your time from founders who've actually done it. There's always more underneath, more questions and more resources, whenever you feel like digging in.
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1
AI in Your ProductShould my startup add AI features, and which ones actually matter to users?
The gist Users do not care that a feature uses AI; they care whether it removes a slow, painful, judgment-heavy step from their day. Start from a specific friction point in your existing workflow, not from the technology, and be honest about whether AI is a checkbox for investors or a real 10x on time saved. The strongest AI features sell the finished work (a resolved ticket, a drafted document), not a fancier tool.
"Should we add AI?" Here is how to decide Vasil (Founder Prompts) A clear decision framework for the exact moment every founder faces: bolt on AI, ignore it, or rethink the product. -
2
AI AgentsWhat actually is an AI agent, in plain language?
The gist An AI agent is software that uses an AI model as its brain to pursue a goal: it plans the steps, uses tools (your email, browser, spreadsheets, code, APIs), checks its own work, and keeps going until the job is done. A chatbot answers you; an agent acts for you. Think of it as a tireless junior teammate that can read, click, write and call other software, but still needs clear instructions and supervision.
Building Effective AI Agents Anthropic The most-cited definition of workflows vs agents, written by the team behind Claude. -
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PromptingWhy do some people get 10x better results from the same AI tools?
The gist Because the output you get depends as much on how you ask as on the model itself, an MIT study found that roughly half the quality gains from a better AI model came from users learning to communicate with it, not from the model. Power users treat AI like a smart new hire: they give it background, examples, and clear success criteria, then iterate on the answer instead of accepting the first draft. The good news is this is a learnable communication habit, not a technical skill.
Study: Generative AI results depend on user prompts as much as models MIT Sloan (Ideas Made to Matter) Hard evidence that half the 'better AI' effect is the user's prompting skill, the clearest answer to why identical tools produce wildly different results.
