How to Get Better Ads, Emails, and Content from AI Using Context Engineering

Patricia Haueiss
Sep 15
3 min read

Context engineering is designing and shaping the input (the “context”) given to a large language model (LLM) so that it produces more:

accurate,
useful, or
aligned outputs.

Instead of just “prompting” with a question:

❌ e.g. “Write me a Facebook ad copy for Black Friday.”

context engineering looks at the entire information environment that the model sees, including instructions, background data, formatting, examples, constraints, and even metadata like roles or personas:

✔️ e.g. “Act as a senior digital marketing strategist. You’re writing ad copy for a women’s swimwear brand targeting 25–40-year-old professionals in Australia. The tone is playful but confident. Avoid clichés like ‘dominate’ or ‘crush it.’”.

Here are the main aspects:

Core Idea

LLMs don’t “know” what’s true and what you want; they predict the most likely next word based on the context you give them. The better you engineer the context, the better the model behaves.

Techniques

Instruction framing: how you word and structure the task request.
Role setting: asking the model to adopt a persona (e.g., “act as a financial advisor”).
In-context learning: providing examples or demonstrations in the prompt so the model mimics the style or reasoning.
Constraint design: embedding rules (“answer in JSON,” “keep responses under 100 words”).
Knowledge injection: supplying relevant facts or data that the model wouldn’t otherwise recall.
Context window management: deciding what to include or exclude when space is limited.

In business or production settings, context engineering is a reliability layer → you can get better performance without retraining a model.

Scenario: Running Black Friday Campaigns

The marketer wants the AI to generate ad copy that’s on-brand, persuasive, and legally compliant.

Without Context Engineering (just prompting)

❌ “Write me Facebook ad copy for Black Friday.”

👉 The LLM might give something generic:❌ "Big discounts this Black Friday! Shop now and save big!"

Not tailored. Not on-brand. Not specific.

✅ With Context Engineering

The marketer builds a richer context before asking the LLM.

1. Instruction Framing

“Act as a senior digital marketing strategist. You’re writing ad copy for a women’s swimwear brand targeting 25–40-year-old professionals in Australia. The tone is playful but confident. Avoid clichés like ‘dominate’ or ‘crush it.’”

2. Inject Brand Guidelines

Target audience: “Australian women, coastal lifestyle, sustainable mindset.”
Brand tone: “Friendly, aspirational, with a touch of humor.”
Words to avoid: “Dominate, secret weapon, insane.”

3. Add Examples (In-Context Learning)

Provide a couple of real brand ads that performed well.

Example Ad: “Sun’s out. New arrivals in swimwear made from 100% recycled fabrics. Sustainable never looked this good.”

4. Add Constraints

Output must fit into 125 characters for headline.
Include CTA at the end.
Generate 3 variations for testing.

5. Knowledge Injection

Supply recent data:

CTR for last campaign = 2.5%
Customers respond 20% better to sustainability mentions than discounts alone.

Result (Context Engineered Output)

"Sustainable swimwear made for summer 🌊 Black Friday deal: 20% off now. Limited stock. Don’t miss your size!"
"Beach vibes, guilt-free style. This Black Friday only → save 20% on recycled swimwear. Shop now."
"Turn heads, not tides 🌏 Eco-swimwear 20% off this weekend. Black Friday never looked this good."

Ideally, you provide examples of copy that performed well. The more relevant, the better.