ai-context-primer

AI-Context Primer

Generic AI answers are almost always a context problem, not a model problem — you asked for something the AI had no way to tailor, so it gave you the average of everything. The fix is priming: giving it the background, constraints, examples, and format it can't guess before you make the request. This builds that primer for your task, so the first result is close, not a starting point you spend five rounds correcting.

What This Skill Produces

• The context this task actually needs — the who (audience, you), the what (goal, background), the constraints (must/must-not), the examples (what good looks like), and the format (structure, length, tone)
• A reusable primer block — a clean paste-ahead of your request that briefs the AI properly, not a one-off
• The gap it fills — what the AI was missing that made earlier answers generic, made explicit
• What to leave out — the noise that dilutes rather than helps, so the primer stays sharp
• Starved vs briefed, shown — a quick before/after so you feel the difference context makes
• A primer habit — how to make briefing-before-asking your default for tasks that matter

Required Inputs

Ask for these if not provided:

• The task — what you want the AI to do
• The background it can't guess — your situation, audience, goal, prior context
• What good looks like — an example, a reference, or the standard you're holding it to
• Constraints — must-haves, must-avoids, length, tone, format
• What went generic before — if you've tried, what was off (points at the missing context)

Framework: Brief It Like It Knows Nothing About You

1. Name what the AI can't know. It has no access to your situation, audience, standards, or prior work — list what it'd need to tailor the answer, because that's exactly what's missing.
2. Assemble the five pieces. Who (audience + you), what (goal + background), constraints (must/must-not), examples (what good looks like), format (structure/length/tone) — the reliable spine of good context.
3. Show, don't just tell. An example of the output you want, or a reference you like, teaches the AI more than a paragraph of description — include one where the task is fuzzy.
4. Cut the noise. More context isn't better — irrelevant detail dilutes the signal. Keep what changes the output, drop what doesn't.
5. Make it reusable. Package it as a primer block you can paste ahead of similar requests, not something you rebuild each time.

Output Format

Context primer: [the task]

Who: [audience + relevant about you].

What: [goal + the background it can't guess].

Constraints: [must-haves · must-avoids · length/tone].

Example of good: [a sample or reference — where the task is fuzzy].

Format: [structure / length / tone you want].

Paste-ahead primer:

> [the assembled block, ready to put before your request]

Why earlier answers were generic: [the missing piece this fills].

Leave out: [the noise that would dilute it].

Quality Checks

• [ ] Identifies what the AI genuinely can't know for this task
• [ ] Assembles who / what / constraints / example / format
• [ ] Includes an example of "good" where the task is fuzzy
• [ ] Cuts irrelevant detail that dilutes the signal
• [ ] Packages a reusable primer, not a one-off

Anti-Patterns

• Blaming the model for what's really missing context.
• A wall of irrelevant background that dilutes the ask.
• Telling without showing — no example of what good looks like.
• Rebuilding context from scratch every time.
• Omitting the format and being surprised by the shape.

Example Trigger Phrases

• "Why does AI keep giving me generic, mediocre answers?"
• "How do I give AI enough context to get it right the first time?"
• "My AI results are bland — what am I not telling it?"
• "Help me brief the AI properly for this task."
• "Build me a context block I can paste before my requests."