Gave (topic)

Gave (the substance you feed an AI) is everything you hand over before asking for output. The transcripts treat it as the input half of a two-sided exchange: what you give the AI, and what you demand back. On the give side, you supply an audience to write toward, the substance it will shape, and your voice or one you admire, so it can match your style.

Why does this matter for AI development? Because a model without context is "just a smart intern who's guessing." The context you provide is mostly data that isn't publicly accessible — your subject-matter expertise, your brain, your intellectual property. That's what makes outputs unique when everyone is using the same model. You also want the AI to generalize a proven process to future tasks, not just repeat the one example you used to prove it out.

Gave also shapes reliability. Chat models are built to give you answers; their sole purpose is to make you happy, so when they don't know something they tend to fill in the blank. If you ask for sources, citations, and quotes, you adjust that incentive so it's acceptable to say "I didn't find anything." Without this, you default to trusting numbers you shouldn't trust. Practically, humans still supply research, taste, and judgment — choosing which problems to point AI at and which results to trust.

To get the best from Claude, stop trying to write one perfect prompt. Anthropic's own teams found they were over-constraining the model, and that removing examples sometimes helped because Claude was more creative than the examples they supplied. That doesn't mean examples are useless — three to five relevant, diverse examples reliably steer format, tone, and structure. Show one normal case and one edge case rather than repeated copies. Explain your reasoning too, so Claude can generalize from the reason instead of imitating one sentence mechanically.

Structure longer prompts with XML tags (labels that separate parts of a prompt), keeping instructions, context, input, and documents in visible compartments you can nest. For analysis, give Claude the source material, ask it to ground findings in that material and quote it, and permit an "insufficient information" answer. For research, demand citations and specify scope and recency. Tell Claude what "good" means in plain language, and name your audience, because a board update needs different depth than an engineering briefing.

Break work into smaller steps. Inside one prompt, tell Claude to work step by step: first extract key figures, then analyze trends, then write the recommendation. For bigger jobs, use prompt chaining (separate messages feeding each other) — research the market, draft the memo, then tighten it. If you don't know the brief, let Claude interview you first, a technique from Anthropic's best practices guide. Always tell Claude to check its work by simply saying "Check your work."

Claude has a well-known flaw: it is the world's biggest yes-man (always agreeing with you). Left alone, it validates what you already said. So push back after any answer by asking for the strongest case against it, or what a smart person who disagrees would say. You also must state your standards in plain language, because Claude has no context for what is appropriate unless you say so. Name your audience, and say what good means instead of hoping Claude infers it.

Vagueness is a common pitfall. Telling Claude to "be helpful and professional" changes nothing. Say specifically how you want it to work. A related mistake is context bloat: when Claude gets something wrong and you say "try this instead," the failed attempt stays in its context and keeps wasting your session limit. If a rule in your Claude.md (a file of standing instructions) is ignored, the file is probably too long and the rule is getting lost.

The key best practice is boundaries. Claude can draft, summarize, analyze, brainstorm, and research — but people stay responsible for consequential decisions. Never let Claude be the sole unreviewed decision-maker in legal, medical, financial, hiring, or safety work. Choose work where a reviewer can catch a wrong answer before harm occurs. A simple rule separates useful assistance from unsafe delegation: keep the consequential external decision human-owned. Risk tells you where assistance needs safeguards, not a faster route to action.