Does Context Engineering Work in Marketing?

Prompt engineering and vibe coding. Two terms that get thrown out a lot online. One is where you phrase your questions in a way that gets just the perfect response from AI models. The other lets people, mainly non-developers, create functions without writing a single line of code.

Now, we have a third one in the mix – context engineering.

Context engineering is the superset of prompt engineering. A methodology where you build systems for large language models (LLMs) that give your AI agents the right information and tools, in the right format, so they can complete a task.

No matter how complex it may be.

Since context engineering works by syncing data, memory, tools, and user intent, it’s a sure-shot way to get results that really matter, and the better your context quality, the better the performance. Currently, it’s being used to create AI coding assistants and service chatbots that speed up product development and customer service.

But what about context engineering in marketing – how does it work? In this article, you’ll find the answers to this question and learn how marketers can use it to reduce workload and achieve personalization at scale.

What Is Context Engineering?

Although relatively new, context engineering is rooted in the principles of managing information flow for AI systems. It’s nothing more than the practice of feeding an AI model everything it needs, whether it’s data, tools, or instructions, so it can do its job reliably, every single time.

Here’s a tweet by Andrej Karpathy, a former member of OpenAI’s founding team, on the nature of context engineering:

“People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step.”

It’s nothing like prompt engineering, where you’d type in a command. After all, a single prompt can only take you so far. Because, what happens when AI needs to recall past conversations, pull user data, summarize past discussions, and then write a personalized email to a user?

Not in parts, but in one workflow?

That’s what context engineering is built for. It shifts the narrative from prompt-centric designs toward holistic systems that give LLM models an “external brain” – complete with structured context, conversation logs, document stores, APIs, and real-time data.

How does context engineering work?

Let’s start with what the “context” in context engineering means. Context is everything AI models refer to before generating an output. And it’s not just the user’s queries; it also includes system prompts on how the agent should behave, along with user interaction records, data fetched on demand, and the tools the model can use.

With the right context, a stateless LLM is transformed into an agent that feels like it “remembers,” “understands,” and “acts” on your directions.

That said, these are the essential components of “context” in context engineering:

• System prompt: Defines the agent’s role, rules, and voice (“You are a marketing assistant…”).
• User prompt: The immediate request or task from the user.
• Short-term memory: A summary or history of the current conversation.
• Long-term memory: User preferences, project archives, and past conversation histories stored in an external knowledge base.
• Retrieval Augmented Generation (RAG): Dynamic fetching of external documents, databases, or APIs to supply up-to-date facts.
• Available tools and definitions: A catalog of callable functions (APIs, code runners, document parsers) with names and descriptions.
• Structured output: Predefined formats (JSON, tables, or markdown templates) guiding the model’s response shape.
• Metadata: Contextual cues like timestamps, user location, or device type.

Here’s how these elements come together. First, the system prompt instructs the AI assistant on how it should behave. Then, the user asks a question. This initiates a data retrieval process that retrieves relevant information, while memory provides both the current conversation summary and past user preferences.

We also have the tools catalogue on standby. For instance, APIs to send emails, databases to query customer records, or parsers to extract PDF content.

All these components merge into a “context bundle” that the model consumes in a single call. The LLM processes everything — conversation history, external data, and tool instructions — as a unified whole. It then produces an output that perfectly aligns with the defined schema, whether that’s a marketing email draft or a product summary table.

Once it generates a response, a feedback loop kicks in. If the response needs adjustments, you can refine the context components. Over time, this iterative tuning ensures that each context bundle becomes more accurate and finely tuned to the user’s needs.

So, why does context engineering matter these days?

LLMs, as we know them, don’t really carry over anything from one interaction to the next. They almost always start each new conversation with no memory of past events.

An agentic AI can’t navigate who the user is or what the task demands without a clear framework to supply relevant context. Instead, it wanders off-topic, recycles errors, or simply takes a guess. Context engineering fills these gaps by assembling everything the model needs into a single, well-structured unit.

With context bundles, you eliminate the four major failures associated with LLMs: hallucinations, statelessness, generic responses, and outdated answers.

• Hallucinations since outputs are based on real data.
• Statelessness as you build short-term and long-term memory systems.
• Generic responses because of user preferences and past interaction data.
• Outdated answers owing to up-to-date documents or API feeds.

Usually, an AI agent fails not because it doesn’t have the right data or tools to do the job, but because the LLM it runs on messes up. It’s largely due to LLMs not getting the right context to generate a good response.

Either the context is missing, or it is formatted badly.

And even the best models won’t help if the quality of the context is poor. So, as your AI workflows grow more complex, following best practices, like defining a clear schema or tagging data sources, ensures that your agent always has the correct information in the right format.

By engineering context, deciding exactly what the agent “sees,” how it sees it, and in what order, you turn a one-prompt text generator into a reliable, multi-step assistant. Best thing about it? It follows your rules and learns from past mistakes.

Context Engineering vs. Prompt Engineering

You might be thinking, “Isn’t context engineering just fancy prompt engineering?” Not quite. Prompt engineering is about choosing the right phrases, keywords, and sentence structures to get the best possible response from an LLM.

Yet, by itself, this method operates in a vacuum: it does not provide the model any additional data, nor does it embed instructions or connect to external tools. All it does is fine-tune a single command without giving the model the intelligence it needs to solve complex challenges.

On the other hand, context engineering intertwines rules, documents, dynamic data sets, and tools into one framework. In marketing, it may involve feeding an LLM a product catalog, brand guidelines, and user behavioral data, along with clear instructions on tone and format.

Infographic from Dex Horthy, posted on X/Twitter.

This doesn’t mean that prompts aren’t important within a context-engineered system.

Creating specific questions helps AI applications navigate their extensive knowledge base properly. But prompts are only a piece of the bigger picture. When you build an AI agent that can recall earlier conversations, pull outside data, or integrate with a CRM, you’re practicing context engineering.

Your prompts guide the model’s actions, but the context supplies the model’s “memory,” data, and rules.

Four Major Applications of Context Engineering

According to Tobi Lutke, the CEO of Shopify, “[Context engineering]... describes the core skill (of prompt engineering) better: the art of providing all the context for the task to be plausibly solvable by the LLM.”

The method allows you to build dynamic systems that can not only access external data but also use outside tools during conversations. They can look up documents, find relevant information using APIs, and include it in the context window alongside the question or task. And the longer you use them, the better they work.

As such, it’s no surprise that context engineering can be used for different purposes, some of which we have covered below.

AI coding assistants

Context engineering is at the centre of modern coding assistants. Windsurf and Cursor are the best examples in this case, merging RAG with agent-like behavior to interact with highly structured, interconnected codebases.

Take a request like refactoring a function. It might seem like you’re just rewriting a few lines. But an AI assistant requires more context. It needs to know where that function is used across the codebase, the data types it handles, how it interacts with external dependencies, and what might break if the logic shifts, even slightly.

Good coding agents are built to handle this complexity. They adapt to your coding style, maintain awareness of project structure and file relationships, and track recent commits to develop a working memory of the system.

Knowledge management

For companies that employ agentic systems internally, context engineering brings together fragmented data silos – CRM records, Jira tickets, internal wikis, and more – to deliver up-to-date answers without overwhelming its human counterparts.

These systems automatically summarize session histories, fetch relevant documents on the fly, and apply personalized rules so that each response aligns with internal guidelines. At the same time, they coordinate memory and task-switching across departments, so the AI can handle layered, multi-step queries and still respond with a single answer.

Customer service chatbot(s)

In customer service, context engineering can be used to build intuitive chatbots and conversational AI systems that guide every customer interaction.

Know that a basic chatbot provides generic responses, sometimes with outdated data.

However, context engineering transforms these basic bots into systems that give a feeling of familiarity. Since they have access to a range of data, like support transcripts, billing queries, user account statuses, preferences, and product documents, they can consistently provide information personalized to the user.

So, in the end, you have support agents who refer to you by name, recall what your previous inquiries were about, and check your account status before recommending a solution.

AI agents

Autonomous AI agents are the next step in context engineering. They go beyond basic RAG systems and become dynamic, goal-oriented entities that can reason, plan, and take action.

These agents don’t just respond to prompts; they solve problems.

Context engineering powers their ability to manage memory, set goals, and use the right tools, even during long or complex sessions. For example, they might call a marketing API to pull ad insights or connect multiple tools to complete a full campaign workflow. They decide which tools to use based on the task at hand in real time.

Rather than giving one-off answers, these agents adapt to changing situations and carry out multi-step tasks in real-world settings. They act like digital coworkers, spotting issues and delivering results with minimal human oversight.

Til now, we’ve covered four use cases of context engineering in different sectors.

So, can context engineering be applied to marketing? Absolutely. In the next section, we’ll look at how it transforms generic outreach into personalized experiences that actually drive engagement.

Context, AI Agents, and Marketing Workflows

A while ago, Christina J. Inge, a marketing instructor at Harvard University, featured Delve AI in her Marketing AI & Analytics News digest on LinkedIn.

To begin with, she spoke about how you can leverage the software to create personas using your website data. What’s interesting is the use case she mentioned later: feeding the personas generated to ChatGPT and prompting it to create content calendars, test messaging strategies, and simulate focus group responses.

This isn’t just an example of another experiment in automation; it’s an exercise in context engineering.

The kind of fusion you see here between personas and generative AI, where one tool feeds data-rich text into another to create a marketing strategy, is what context engineering in marketing is all about.

When this approach is combined with agents — autonomous AI entities capable of using tools and making decisions with minimal human involvement — it becomes possible to automate large chunks of your marketing workflows without losing the strategic depth you'd expect from a human-led team.

This might mean running an end-to-end email campaign.

The email agent won’t just write emails; it’ll segment your audience, check your calendar, draw on your CRM, adapt to promotions, and measure engagement. It will use every tool and data point available to accomplish your marketing goals.

But unlike a human, it won’t get overwhelmed when managing thousands of customers. It won’t forget details or lose context.

What context engineering means for marketers...

It means creating a marketing agent, or a copilot, running on a system that has the relevant context: your audience profiles, campaign performance data, brand assets, competitive analysis, business objectives, market signals, and more.

So that it’s not just a generic text generator but a dynamic, context-aware assistant.

Prompt engineering is old news. Marketers need to move beyond surface-level use of tools like ChatGPT and Claude, and increase engagement and conversions with context engineering.

Instead of looking at data, functions, and tools as isolated components, you need to build an interconnected, holistic system that understands: who the customer is, where they are in the journey, what the brand stands for, and how past campaigns have performed to complete tasks and make decisions.

Once you’re done building context, unifying assets, tools, market data, rules, and business KPIs, your AI agent can:

• Create marketing materials adhering to the brand voice and messaging.
• Deliver personalized content and product recommendations based on browsing history and purchase behavior.
• Leverage real-time data like location or weather to give out discounts or coupons.
• Automate repetitive marketing tasks like lead capture, follow-ups, and nurture flows.
• Predict user needs and recommend the next-best product or content before the user requests it.

These AI agents give you the ability to personalize everything at scale and automatically optimize your marketing plans based on real-time market data, with a responsive feedback loop that keeps on getting better.

The Problems with Context Engineering

Although context engineering offers big benefits, it also brings in challenges that need smart solutions to work well.

1. Context poisoning

When AI hallucinates or misinterprets its training data, that bad response can sneak into its context. From that point on, the system may keep citing or building on false details. Over time, these errors stack up, and removing them becomes a hassle.

Fix: Build in strict validation checks and versioned context stores. When you spot a bad snippet, you can roll back or replace only that fragment, without wiping out everything else.

2. Context distraction

As you start feeding more data to the model, situations may come up where it starts focusing on accumulated history. Instead of drawing on its training, it loops over old replies.

Fix: Use context summarization and abstraction. Periodically compress long histories into concise summaries. This keeps the AI focused on fresh, relevant info.

3. Context confusion

Overloading the context with extra information can lead to mixed-up answers. Irrelevant data, like marketing guidelines when you’re drafting a support email, can steer the AI off-course. You end up with responses that mix up two or more tasks.

Fix: Apply context filters. Before each call, filter out any unrelated documents or tool descriptions so the model only “sees” what it needs for that task.

4. Context clash

Sometimes, two sources in the context contradict each other, say, two versions of a product spec or outdated pricing versus current rates. The AI then has to guess which one to trust, which leads to muddled replies.

Fix: Use context pruning. Regularly scan for outdated or conflicting entries and remove them. This keeps the “memory” coherent and reliable.

Tackling these challenges is the key to building marketing workflows that are efficient and accurate.

A Way Forward

Context engineering is one of the most essential skills in the age of AI. It has, quite obviously, surpassed traditional prompt engineering, giving its users a significant advantage over its competitors.

You don’t need a perfect prompt to write ad copy anymore; you need a smart system that manages and executes entire campaigns.

With context engineering and AI agents, you can personalize content at scale, adapt to evolving customer preferences, and deliver domain-specific answers that build brand value, with zero manual input.

This isn’t some far-off future. With context engineering, you can build workflows where AI agents work like marketing assistants. They know your customers, speak your brand language, and make data-driven decisions.

Of course, there are still people at the center, guiding the agent and refining campaign results. But the repetitive, context-heavy tasks? That can now be handled by your AI-powered colleagues.

And for marketers who want to move fast without losing nuance, that’s a big win.