Most marketing teams are stuck in the AI hamster wheel.
You open ChatGPT. You spend 10 minutes briefing it on your company, your audience, your brand voice. You get a decent first draft. Then you close the tab and the AI forgets everything.
Tomorrow, you start from scratch.
According to the 2026 State of AI for GTM report, 53% of GTM leaders report little to no impact from AI. Nearly half don’t have a single AI agent in production. The problem isn’t the models — it’s the workflow.
Table of contents
Open Table of contents
What is context engineering?
Context engineering is the practice of designing the entire information environment around an AI model — not just the prompt you type, but the knowledge, data, tools, and memory that shape every response.
Tobi Lütke, CEO of Shopify, put it simply:
“I really like the term ‘context engineering’ over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.”
Tobi Lütke, CEO of Shopify
Andrej Karpathy, former Tesla AI lead and OpenAI founding member, expanded on this:
“Context engineering is the delicate art and science of filling the context window with just the right information for the next step.”
Andrej Karpathy
Think about the difference this way. Prompt engineering asks: “How do I phrase this question?” Context engineering asks: “What does the AI need to know before I even ask?”
Why prompt engineering hits a ceiling
Prompt engineering was a great starting point. It taught marketers that how you ask matters. But it has a fundamental limitation: it treats every AI interaction as a one-off conversation.
A prompt can tell the AI to “write a LinkedIn post in a conversational tone for B2B SaaS marketers.” That’s useful. But it can’t tell the AI about your last three campaigns, what messaging landed with your ICP, which channels are underperforming, or what your CEO said about positioning in last week’s all-hands.
As Maja Voje describes, a marketing manager using ChatGPT for cold emails might spend 30 minutes and a dozen rounds of back-and-forth to get something usable — then lose all of that refinement when the session closes.
The teams seeing real returns from AI aren’t writing better prompts. They’re building better context.
The four building blocks of context engineering
Voje outlines a practical framework for building a context engineering system. While the specifics reference Claude Code (Anthropic’s AI coding tool), the principles apply to any AI workflow.
1. A persistent knowledge base
The foundation of context engineering is a document that the AI reads at the start of every session. This isn’t a one-off brief — it’s a living document that includes:
- Your company positioning and value proposition
- Ideal customer profiles with real pain points
- Brand voice rules (3-5 concrete guidelines, not vague adjectives)
- Current campaign priorities and learnings
- Links to supporting resources
This means every interaction starts with the AI already understanding who you are, who you serve, and how you communicate. No more re-briefing.
2. Reusable playbooks
Instead of writing custom prompts for every task, context engineering uses encoded playbooks — standardised processes for repeatable marketing tasks. These capture your best practices, quality standards, and proven frameworks so every output follows the same standard.
For example, a “campaign brief” playbook might include your brief template, required fields, past examples of high-performing briefs, and specific criteria for approval. The AI follows the playbook every time, producing consistent results without the marketer reinventing the process.
3. Live tool connections
The most powerful context isn’t what you type — it’s what the AI can access directly. Model Context Protocol (MCP) and similar integrations let AI pull live data from your CRM, analytics platforms, and marketing tools without manual copy-pasting.
Instead of telling the AI “our open rate was 24% last month,” the AI reads it directly from your email platform. This eliminates the biggest source of AI errors: stale or incomplete information provided by the human.
4. Automated quality gates
The final layer is automatic checks that run before or after AI output. These enforce standards without manual review:
- Formatting and style checks
- Compliance flags for regulated industries
- Approval workflows for sensitive content
- Notifications when tasks complete
Quality gates turn AI from a tool that requires constant supervision into a system you can trust.
The compounding effect
The real power of context engineering is that it compounds. Voje maps out what this looks like over time:
- Week 1: Your knowledge base eliminates the daily re-briefing. Output relevance improves immediately.
- Month 1: First playbooks created, tools connected. The system starts learning from real work patterns.
- Month 3: Campaign learnings, refined ICPs, and tested messaging angles accumulate. Every new task starts from a higher baseline.
- Month 6: A living knowledge base emerges that captures institutional learnings and informs all future work.
This is the gap between teams getting real value from AI and teams that gave up after a few underwhelming ChatGPT sessions. It’s not model quality or prompt sophistication. It’s infrastructure.
Getting started: what to do this week
You don’t need to build the whole system at once. Start with the highest-leverage piece: your persistent knowledge base.
Create a document — even a simple text file — that captures:
- Who you serve and what makes you different
- Buyer personas with real pain points (not demographics)
- Brand voice rules — 3-5 concrete, specific guidelines
- Recent campaign learnings — what worked, what didn’t, and why
- Your current marketing stack and how tools connect
Feed this to your AI tool at the start of each session. You’ll notice the difference immediately.
From there, identify your most repetitive marketing task — campaign briefs, weekly reports, content outlines — and build your first playbook. Document the process, the quality criteria, and a few examples of good output.
The bottom line
Context engineering isn’t a buzzword. It’s the practical difference between AI that wastes your time and AI that genuinely accelerates your marketing.
The shift is simple in concept: stop treating AI like a chatbot and start treating it like a new team member who needs proper onboarding, clear processes, and access to the right tools and information.
Marketing teams that build these systems now will compound their advantage every quarter. Those who keep starting every AI conversation from scratch will keep wondering why the robots aren’t delivering.
Growth Method is the GrowthOS built for marketing teams focused on pipeline — not projects. Book a call at https://cal.com/stuartb/30min.
“We are on-track to deliver a 43% increase in inbound leads this year. There is no doubt the adoption of Growth Method is the primary driver behind these results.”
Laura Perrott, Colt Technology Services