Here's the thing nobody tells you about AI: the model isn't the problem.
You are smart. You are capable. You've probably been using ChatGPT or Claude every single day, grinding through prompts, rewriting 80% of what comes back, and wondering why it still sounds like it was written for someone else's business.
That's not a you problem. That's a context problem. And it's fixable — today, in thirty minutes, with a system you build once and reuse forever.
I'm going to walk you through building your first Context Stack right now. Not theory. Not a course. Just the template, the thinking behind each layer, and real examples so you can see exactly what "done" looks like.
Set a timer. You'll be done before it goes off.
A Context Stack is a 6-layer document that onboards AI to your business. Think of it like this: if you hired a freelancer and gave them zero briefing — no audience, no budget, no constraints, no goals — you'd get back generic work. You'd never do that to a human. But most of us do it to AI every single day.
The Context Stack is the briefing. You paste it into any AI tool before your actual request, and the model finally has what it needs to write for your business instead of guessing.
Six layers. Each one teaches the model something specific:
- Role — Who the AI is acting as
- Objective — The specific, measurable outcome
- Business Context — Your real-world situation
- Constraints — What the AI must respect
- Output Structure — The exact format you need
- Risk Guardrails — What the AI must never do
This isn't complicated. But it is powerful. Let's build yours.
The Blank Template
Copy this right now. Paste it into a doc, a Notion page, whatever you reach for every day. We're going to fill it in together.
CONTEXT STACK™ v1
Role:
Objective:
Business Context:
Constraints:
Output Structure:
Risk Guardrails:
Six fields. That's the whole thing. Now let's make it yours.
Layer 1: Role (2 minutes)
The Role layer sets the thinking level. Without it, the model defaults to "helpful assistant" — and that's exactly the quality of output you get back. Helpful. Generic. Forgettable.
The pattern: "You are a [seniority level] [function] for a [type of company]."
Examples:
- "You are an executive-level marketing operator for a service-based SMB."
- "You are a senior content strategist for a B2B SaaS company."
- "You are a fractional CMO advising a 20-person professional services firm."
What to avoid: Vague roles like "marketing expert" or just "assistant." The more specific you are, the more the model rises to match. A "senior strategist" genuinely produces different output than a "marketing helper" — even with the exact same prompt.
You're not tricking the model. You're telling it what standard to operate at. That matters more than you'd think.
Go fill in your Role. Two minutes. You've got this.
Layer 2: Objective (3 minutes)
This is where you anchor the output to a real business goal. Without an objective, the model writes to fill space — three pretty paragraphs of suggestions that don't actually move anything forward.
The pattern: "[Action verb] + [specific metric] + [timeframe]"
Examples:
- "Generate 15 booked service calls from past customers in 14 days through a 3-email reactivation sequence."
- "Create a 30-day lead gen campaign brief targeting CFOs at $10M–$50M companies. Target: 20 qualified discovery calls."
- "Write 4 LinkedIn posts that drive 50+ profile visits per week from in-house marketing managers."
The test: Could you look at the AI's output and say "yes, this moves us toward the goal" or "no, it doesn't"? If the answer isn't obvious, your objective needs more teeth.
"Promote our service" isn't an objective. "Book 15 calls in 14 days" is. Make it measurable and the output sharpens instantly.
Fill in your Objective. Three minutes. Keep going.
Layer 3: Business Context (8 minutes)
This is the big one. This is where most of your Context Debt lives — and it's the reason AI sounds like it was written for a stranger.
Here's what's happening: you carry critical business reality in your head every day. Your audience. Your capacity. Your positioning. What "good" actually looks like for your organization. You know all of this cold. AI doesn't have a single piece of it — unless you put it there.
What belongs here:
- What your company does (plain language, not marketing copy)
- Who your customers are (specific, not "everyone")
- Your market position and differentiators
- Team size and capacity
- Key metrics (average deal size, customer count, service area)
- Seasonal or timing factors
- Anything a competent freelancer would need on day one
Example:
Business Context:
- Family-owned HVAC company, 12 years in business
- Service area: 30-mile radius of [city]
- 4.8 stars on Google (200+ reviews)
- Seasonal demand: spring tune-ups are our biggest revenue window
- Team capacity: max 8 appointments per day
- Average ticket: $180–$350
- Past customer list: ~2,400 emails (opted in)
The rule: If you'd correct the AI for getting it wrong, it belongs here. If the model would need to guess, put it in writing so it doesn't have to.
This layer takes the longest. That's completely normal — you're pulling knowledge out of your head and putting it somewhere the model can actually use it. That's not easy. But once it's done, it's done. Every AI interaction after this gets better for free.
Take eight minutes. Be specific. You're doing the hardest part right now.
Layer 4: Constraints (5 minutes)
Here's something people miss: constraints aren't limitations. They're instructions. They tell the AI what boundaries matter in your world — pricing rules, legal requirements, tone, word counts, things to avoid.
Without constraints, the model will over-promise, ignore your limits, and produce copy that your legal team or your boss would reject in five seconds. With them, the output stays inside the lines your business actually operates in.
What belongs here:
- Pricing rules (no discounts, must mention financing, etc.)
- Legal or compliance requirements
- Tone and voice guidelines
- Word count or format limits
- Things the AI must always include
- Things the AI must never say
Example:
Constraints:
- No discounts or percentage-off offers (brand positioning)
- Must mention financing option (approved by legal)
- No same-day service promises — earliest next-day
- Emails must be 150–180 words max (our audience skims)
- Tone: direct, warm, zero hype — like a trusted neighbor
- Must include a clear CTA with booking link
Common mistake: Leaving constraints out because they feel "obvious." They're obvious to you. They're invisible to the model. If it matters, write it down.
Five minutes. You're past the halfway mark.
Layer 5: Output Structure (5 minutes)
Without this layer, you get what I call paragraph soup — three blocks of flowing prose with no sections, no headers, and nothing you can actually use without reformatting the whole thing.
Output Structure tells the AI exactly what format to deliver. Sections. Order. Format. When the model knows the structure, it allocates attention across every section instead of front-loading the first paragraph and fading out.
Examples:
For an email:
Output Structure:
Subject → Headline/Hook (1 line) → Offer (2 bullets) → Proof (1–2 bullets) → Objection reducer (1 line) → CTA (1 line) → P.S. (optional)
For a campaign brief:
Output Structure:
Objective → ICP Profile → Key Messages (3) → Channel Plan → Asset List → 30-Day Timeline → Measurement Plan → Risks/Guardrails
For a social post:
Output Structure:
Hook (1 line, pattern interrupt) → Insight (2–3 lines) → Proof or example (1–2 lines) → CTA (1 line)
Pick the output format you use most often. Write it out. You'll swap in different structures for different tasks over time — but start with the one you reach for daily.
Five minutes. Almost there.
Layer 6: Risk Guardrails (5 minutes)
This one is about trust. AI is confidently right about 70% of the time. The other 30% — wrong numbers, invented quotes, claims that would get flagged by legal, tone shifts that don't sound like you — that's the dangerous part. And you can't always tell which 30% you're looking at.
Guardrails catch that. They're the safety net that lets you move faster because you're not second-guessing every line.
Start with these — they apply to almost every business:
Risk Guardrails:
- Do not invent statistics, case studies, or customer quotes
- Do not promise specific ROI, savings, or timelines unless provided
- List all assumptions separately at the end
- If key information is missing, ask up to 3 questions before generating
- Flag any section where provided context is insufficient
Then add your own. Think about the last time AI output made you wince — whatever caused that reaction, add a guardrail for it. Industry compliance? Competitor mentions? Pricing claims? Put it in writing.
Five minutes. And you're done.
Your Completed Stack
Look at what you just built. Six layers. Your business reality organized in a format AI can actually use. Here's what a finished one looks like for a B2B digital agency:
CONTEXT STACK™ v1
Role:
You are a senior marketing strategist for a digital marketing agency
that serves mid-market B2B companies.
Objective:
Create a 30-day lead generation campaign brief targeting CFOs at
companies with $10M–$50M revenue who need fractional CMO services.
Target: 20 qualified discovery calls.
Business Context:
- Agency: 14 people, fully remote, founded 2019
- Core offer: Fractional CMO engagements ($8K–$15K/month retainer)
- ICP: B2B companies, $10M–$50M revenue, no in-house marketing leader
- Differentiator: We embed inside the client's team (not external vendor)
- Current pipeline: 60% referral, 40% outbound — goal is increase inbound
- We have 6 published case studies with named clients and metrics
Constraints:
- No "agency speak" — no jargon like "synergize," "leverage," "holistic"
- Must reference at least 2 case studies with real metrics
- Budget: $5K for the 30-day test
- Channels: LinkedIn organic + email only (no paid ads this round)
- Tone: peer-to-peer executive, not salesy
- All claims must be verifiable from our case study library
Output Structure:
Campaign Brief: Objective → ICP Profile → Key Messages (3) →
Channel Plan → Asset List → 30-Day Timeline → Measurement Plan →
Risks/Guardrails
Risk Guardrails:
- Do not invent case study results — use only what's provided
- Do not promise specific ROI timelines
- Do not reference competitors by name
- List assumptions separately
- Flag any section where provided context is insufficient
Save yours somewhere you reach for every day. This is the document that makes every AI interaction from here forward work for your business instead of a stranger's.
How to Use It
The workflow is simple. Four steps, every time:
- Open your AI tool
- Paste your Context Stack at the top
- Add your specific request below it
- Hit enter
That's it. No special syntax. No plugins. No Custom GPT. Just your context, then your request.
Your first test: Take whatever you'd normally ask AI to do today — an email, a campaign brief, a social post. Run it twice. Once the way you usually do it. Once with your Context Stack pasted in first. Compare the two outputs side by side.
The difference will be obvious. Not subtle. Not incremental. Obviously different. That's the moment you realize the model was never the problem.
What to Update (and When)
Your Context Stack isn't a one-and-done artifact that sits in a drawer. It's a living document. Update it when:
- Your offer changes (new pricing, new service, new positioning)
- Your audience shifts (new ICP, new segment, new vertical)
- You learn a new constraint (legal updated compliance language, capacity changed)
- AI gets something wrong that a guardrail should have caught
Most updates take under two minutes. The structure stays the same — you're just swapping details inside the layers. That's the whole point: build the architecture once, update the details as your business evolves.
What You Just Did
You just built something most people never build. Most AI users spend months tweaking prompts, trying different models, buying prompt packs — and never address the root cause. You just did. In thirty minutes.
Every AI interaction from here forward starts with context instead of guessing. That's not a small thing. That's a fundamentally different way of working with AI.
The shift from prompting to engineering starts right here. And you just made it.
→ Read the complete guide to Context Architecture
→ Get the full Context Stack system with templates — $37
Chris Battis is the founder of PromptSquad and an AI Solutions Architect who has designed systems for Google, iHeart Media, Home Depot, and Wayfair. The Context Stack translates enterprise-grade context architecture into a system any marketing operator can use in 60 minutes.
