Context Engineering Simplified for Radio

Part One: What is it?

Context Engineering is about helping the AI understand the assignment and ensuring it has everything it needs before it starts.

Although this explanation is quite simple, it is accurate. It means giving an AI the right information, instructions, examples, rules, and goals so it can give a better answer or do a better job.

A broader definition is that context engineering is essential for AI agents to work properly and consistently over time. We'll purposefully avoid a high-tech, mind-bending explanation of the implications and design of context engineering for agents. So that you can apply the concept easily, all you need to know for now is that context engineering involves multiple elements that control what information is available to an agent and how and when it is used.

Prompt engineering is the intentional design of a prompt. The method you use, how you write, organize, and instruct an LLM, like ChatGPT, in order to get a good response. An example is following the gold standard of prompt engineering:

-Role: You are a …
-Objective: Your job is to …
-Context: (how the response will be used, who it’s for, etc.)
-Constraints/Guardrails: You must … (always/never)
-Output format: (script, bullet points, social post, paragraph, etc.)

Context engineering is setting up everything the AI needs before it answers. It’s essentially giving the AI specific instructions for the task, access to the knowledge it needs, examples of what good looks like, and a purpose.

Context engineering is like sitting down to write a promo about something you understand very well. Example:

You’re writing for a contest to give away concert tickets to a big show coming up in a few months. Keywords will be given out every afternoon starting tomorrow, announced at the end of the artist's current big single. You know the station's name, positioning statement, brand identity, and the writing style that fits the brand image and target listener expectations. You also know the order of the information should flow through the promo, how long it should be, and that you’ll need to write updated versions for tomorrow, weekends, and weekdays.

Imagine having to engineer a prompt that writes that promo! It took almost over 100 words just to describe it. Think of all the things the AI would need to have access to and knowledge of to be able to write that promo by just typing in the prompt: “Write a contest promo with all necessary updates.”

Learning prompt engineering teaches you how to talk to AI. It’s important, clearly. But something bigger is emerging right now: the context engineering era of AI. Understanding its basic concepts teaches you how to think with AI.

This is the evolution from thinking about how to write a prompt to thinking of AI as a reasoning engine with the right context, information, and tools.

In part 2 of Context Engineering Simplified for Radio, we’ll make it easy for you to take the first step in this fundamental shift from optimizing sentences to optimizing knowledge.

Ensuring the AI has the direction and knowledge to handle complex, multi-step tasks, rather than just answering questions, matters for anyone in radio who uses AI. And, you’ll love just how simple it is to get your first win with context engineering.