How LLMs Process Your Marketing Content
Understanding the mechanics of tokenisation, context windows, and training data helps you write better prompts and debug outputs that miss the mark. This lesson is practical, not technical.
In practice: Campaign briefs: 3 hours → 20 minutes
Your version of this lesson adapts to your role. After the 3-minute assessment, examples, scenarios, and exercises are tailored specifically to your job function and experience level.
Personalise →Why Understanding Mechanics Makes You Better at Prompting
You don't need to understand how a car engine works to drive. But knowing why your car struggles uphill changes your driving habits. The same applies to AI. Understanding two key concepts — the context window and training data — will directly improve your results.
The Context Window: AI's Working Memory
Every AI conversation happens inside a "context window" — the amount of text the model can see at once. Think of it as the model's working memory. If your conversation gets very long, older messages may fall outside this window and the AI effectively "forgets" them.
Practical implications:
- ■For long projects (e.g., rewriting an entire website), break work into sections rather than dumping everything into one conversation
- ■If the AI's responses seem to drift or lose track of earlier instructions, start a fresh conversation with a summary of key constraints
- ■Place the most important instructions (brand voice, audience, format) at the beginning of your prompt, not the end
Training Data and the Cutoff Problem
LLMs are trained on text scraped from the internet up to a specific date. After that cutoff, they have no knowledge of new events, products, or market changes.
What this means for marketers:
- ■Don't ask AI for this year's industry statistics — verify independently
- ■AI won't know about your competitor's product launch from last month
- ■Trend-based content ("what's trending in marketing right now") will be stale
The workaround: Paste current information into your prompt. If you have a recent report, share the key data and ask the AI to incorporate it. This is called "retrieval" and it's highly effective.
Tokens, Not Words
AI processes text in "tokens" — roughly ¾ of a word on average. A 1,000-word document is approximately 1,300 tokens. This matters for:
- ■Understanding pricing (most paid tiers charge per token)
- ■Knowing why very long documents get truncated
The Practical Golden Rules
- ■Front-load your instructions — most important constraints go first
- ■Provide context the AI can't have — paste in current data, your brand guidelines, your audience description
- ■Work in sections for long-form projects — don't try to produce a 3,000-word piece in one prompt
Key Takeaways
- ■The context window is the AI's working memory — long conversations may cause it to "forget" earlier instructions
- ■Front-load important constraints (tone, audience, format) at the start of every prompt
- ■Training data has a cutoff date — always verify current statistics and recent events
- ■Paste current information directly into your prompt to get AI to work with up-to-date data
- ■Break long projects into sections rather than attempting one enormous generation
Before you practise
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