How I Used Context Engineering to Complete Marketing Tasks 3x Faster

Context engineering is the practice of designing prompts, inputs, or environments to optimize how large language models (LLMs) interpret and respond to information.

It guides AI behavior by manipulating a prompt’s surrounding context, ensuring outputs are relevant, accurate, and aligned with user intent.

And it’s the newest hot take in the AI space. In Exploding Topics, searches for context engineering have increased dramatically in 2025, spiking 1900%:

Whereas prompt engineering focuses on the specific instructions you give an AI, context engineering is about architecting the entire environment of information that surrounds those instructions.

Context engineering is most relevant to developers. But as a marketer, you may have already done something similar if you’ve uploaded documents to a Claude project, for example.

This is very much like prompt engineering…just to a further degree. You’re providing more context with your prompt so that the LLM’s output is what you need it to be–the first time.

After looking at a lot of different ways industry experts have described context engineering, my favorite is Shopify CEO Tobi Lutke: “The art of providing all the context for the task to be plausibly solvable by the LLM.”

For marketers, context engineering has two important outcomes: improved efficiency and better results from AI-assisted workflows.

Next, let’s go over 4 real examples you can use in and steps you can take to add context engineering to your day-to-day marketing playbook. I’m using Claude, but you can use your AI tool of choice.

First, Provide Historical Performance Metrics

Don’t start from scratch every time you’re using your AI for a marketing task. I connect the LLM to (or feed it) historical data to start with necessary context every time!

Then, move on to…

1. Fix Content Decay

I have experience with this one. I created a system in Claude Pro Projects to update articles at least 3x faster than before.

Why? Because content continuously loses visibility in organic search the older it gets. This concept is called “content decay.”

With context engineering, though, you can create a system to identify articles that show signs of decay AND update them quickly.

Here’s what I did:

• Uploaded traffic data from Google Search Console and Google Analytics to identify the best articles to update now
• Created a brand + my voice document to help it write in the correct style and tone
• Added multiple examples of past blog posts of different types (listicles, how-tos, ultimate guides, etc.)
• Included a one-pager on our audience and product
• Gave the project specific instructions so that every time I pasted a blog post in + keywords/topics I wanted it optimized for, it would automatically grab the context it had from my uploads and update the post.

With that much context, a quick once-through edit was usually all the update needed before hitting “Publish.”

To create your own context-engineered project like this, here’s an example of what I used in the “Project knowledge” box:

Then, your prompts can be as short and sweet as this: keywords, article. That’s it.

2. Create Ready-to-Run Campaign Briefs

Instead of asking AI to "create a social media campaign" or “write ads for my next Google Ads campaign,” context engineering means providing data first so that the AI truly understands your business, audience, and other needs:

• Target Audience Context: Demographics, pain points, intent, and decision-making factors
• Brand Context: Voice, positioning, and content guidelines
• Competitive Context: Key competitors, their messaging strategies, and differentiation opportunities
• Performance Context: Historical campaign data, conversion benchmarks, and success metrics
• Channel Context: Platform-specific requirements, audience behaviors, and format constraints
• Business Context: Campaign objectives, budget constraints, and timeline requirements

With context engineering, you can go from this incredibly simple prompt:

To this prompt with tons of context, including reference PDFs:

3. Automate Keyword Research and Content Gap Analysis

Instead of manually researching keywords or trying to figure out what content to create next, you can build a context-rich system that identifies keyword and topic opportunities with less effort from you.

Set up your AI with:

• Your current content list plus performance data
• Competitor content analysis and keyword gaps
• Your target audience's search behavior and intent signals
• Business objectives and content marketing goals

Then ask it to cross-reference everything and identify the highest-impact content opportunities. For example:

You can even have to cluster keywords for you:

4. Create Email Nurture Sequences That Delight & Convert

Instead of generic email sequences that treat all subscribers the same, use context engineering to build sophisticated nurture campaigns that adapt to different subscriber behaviors and characteristics.

Set up your AI with:

• Segmentation data showing subscriber sources, behaviors, and engagement patterns
• Customer journey mapping with specific pain points at each stage
• High-performing email examples from your archives with open rates and click-through data
• Your style and tone information
• A/B testing results showing which subject lines, CTAs, and content formats work best
• Customer interview insights (if available) about decision-making factors and timing
• Sales team feedback on common objections and questions prospects ask

Then prompt your AI with something like:

These are just a few examples, but the actual capabilities of context engineering for marketers are vast. Pretty much any marketing task you can dream up, you can improve with context engineering.

Prompt Engineering vs. Context Engineering

The main difference between prompt engineering and context engineering is the way you manipulate the LLM to spit out your desired output.

Early AI interactions were mostly simple prompts: ask a question, get an answer. But as everyone started using AI for more complex tasks, the limitations of basic prompting quickly became obvious.

To make the answers a bit more usable, we started giving the LLMs more information. We engineered the prompts we were using in order to get an output that was actually a bit closer to our standards.

We even started using prompts to engineer our prompts!

This was prompt engineering, and it’s still a pretty popular concept:

With context engineering, as I demonstrated, you provide extra, supporting information that the LLM needs to improve its response. You’re expanding the context the LLM has access to.

Prompt engineering focuses on….

Context engineering focuses on….

Crafting clear, specific, goal-oriented instructions Finding the right wording to trigger desired responses Optimizing single interactions Iterating on prompt wording to improve output Providing explicit instructions and constraints

Designing comprehensive information systems to provide context Providing relevant background knowledge (e.g., documents, user data, conversation history) Using memory, embeddings, or retrieval systems to enrich responses with external knowledge Personalizing outputs based on user preferences, brand guidelines, or prior interactions Integrating tools or APIs the model can use (e.g., web search, calculators)

Here’s an example of a marketing prompt that’s unengineered, then prompt engineered, and finally context engineered.

Unengineered:

Prompt engineered:

Context engineered:

System / Context Setup:

(These are background references the AI uses to shape its output.)

• BrandGuidelines.pdf – Outlines the brand voice as professional yet approachable, with a preference for short sentences and real-world examples.
• ContentMarketing_CaseStudies.docx – Includes detailed examples of successful content marketing campaigns from HubSpot, Mailchimp, and a local B2B SaaS company.
• TargetAudiencePersona.pdf – Describes the target reader as a mid-level marketing manager at a small to midsize business.
• Model Memory – The user frequently writes content for marketing blogs and prefers SEO-optimized formatting (H2s, bullets) and value-dense writing. The LLM has already been told to remember this.

User Prompt

Context Engineering vs. Vibe Marketing

Context engineering is a key component of vibe marketing. We're learning to set up environments where AI can actually understand our business context and provide a lot more value.

And instead of focusing on individual conversations, we can build entire knowledge systems that make AI genuinely useful for many of our marketing objectives and goals.

So, no more trying to trick AI into giving us what we want through clever wording.

In the future, you might have all of your data connected to AI, rather than needing to feed it manually. It might have access to your CRM, docs, customer research…everything.

Here’s a real example from Lani Assaf of Anthropic: she creates a “board of brains” using context engineering.

Stay Updated With AI Marketing Trends

Context engineering sounds like a technical topic. But for marketers, it could make the difference between mediocre AI assistance and great results.

And as Brian Dean said: “AI Is an SEO cheat code (if you know how to use it)”.

Context engineering is one of the many AI trends that we’re tracking. To get access to the same data we use, sign up for Exploding Topics today and be the first to catch AI trends on the ascent.