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
- 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.
- 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.
- 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.
- Cut the noise. More context isn't better — irrelevant detail dilutes the signal. Keep what changes the output, drop what doesn't.
- 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."
