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Context Engineering: Optimizing AI like a Soccer Player

Context Engineering: Optimizing AI like a Soccer Player

Amalinali Pacheco
Carlos Cesar
August 17, 2025

Overview

This article explores the concept of Context Engineering through a simple yet powerful analogy: a soccer player warming up before entering the field. Just as warming up prepares the athlete to perform at their best, providing prior context to artificial intelligence enables it to deliver more precise, coherent, and useful responses. The article explains the phases of this preparation, the common mistakes when skipping it, and practical examples to apply it right away. The goal: to demonstrate how a small investment in context can transform your AI interaction into a high-performance game.

Introduction – The Analogy Everyone Understands

Imagine you’re about to play an important match. The coach calls you, you walk out of the locker room straight onto the field
 no warm-up, no stretching, no feeling the ball. You know what’s coming: heavy legs, slow reflexes
 and performance far below what you’re capable of. The result will be a disaster.

With artificial intelligence, the exact same thing happens. If you open a chat and ask for something complex without giving the proper context, the AI "comes in cold" and will give you generic or incomplete answers. Just as a player needs to warm up to perform at their best, AI needs prior information so its "muscles" —its networks and connections— are ready to act.

In the technical world, this prior preparation is known as Context Engineering: the art of structuring and providing initial information to an AI model so it can respond with greater accuracy, coherence, and relevance. At Notecraft, we’ve decided to explain it with an analogy you’ll never forget: context as the warm-up of a soccer player before stepping onto the field.

What is context preparation (and why does it matter)

Prior contextualization means providing the AI with an initial load of key information before making the main request. This practice allows the AI to:

  • Accurately understand the topic of the conversation
  • Be aware of background, data, and relevant details
  • Avoid misunderstandings and off-target answers

Starting an interaction without prior context is like interrupting a conversation with a question that lacks shared background. On the other hand, beginning with well-defined context is like catching up before addressing a relevant issue.

Example:

  • Direct request: "Give me a summary of Chapter 4."
  • Request with context: "We’re working with book X. We’ve already summarized chapters 1, 2, and 3. Chapter 4 is about [topic]. Here’s the full text. Now give me a summary."

The analogy in three phases

1. Light warm-up

You give the AI general information about the project, topic, or problem. You don’t go into details—just define the framework.

Example: "We’re creating an article about artificial intelligence applied to education. So far, we’ve talked about personalized learning and interactive tools."

2. Intense warm-up

Now you provide concrete data: text fragments, previous summaries, key points. The AI begins to "activate" its connections with relevant information.

Example: "Here’s the outline we have so far: introduction, use cases, benefits, challenges, and conclusion. These are the key points for each section."

3. Entering the match

With the context already loaded, you make the main request.

Example: "With everything above, draft the section on benefits in a motivating tone and with practical examples."

Common mistakes when skipping context

  • Generic answers: the AI doesn’t know you’re looking for something specific and replies with the most common response.
  • Repetitive questions: you end up rephrasing the same request because the first attempt didn’t work.
  • Inconsistency: if the topic is complex, the AI may contradict itself because it doesn’t have all the information together.

In short, you waste the tool’s potential and end up working more than necessary.

How to apply it starting today

Context setting is not a hidden trick nor does it require technical knowledge. It’s a practice you can adopt right away.

Example of a prior prompt: "I’m going to ask for your help in developing a guide on study habits for university students. Before giving you the final instruction, here’s the context: this article will be published on an educational blog, it’s aimed at students aged 18 to 25, and we want a friendly yet serious tone. The content should include practical examples and avoid jargon. Ready?"

After this, your final request will have a much greater impact. Cases where it shines:

  • Writing articles or guides
  • Creating project plans
  • Developing code with multiple requirements
  • Preparing classes, workshops, or presentations

Strategic Reflection

Just as no professional soccer player underestimates their warm-up, no user aiming to get accurate answers from an AI should skip context. It’s a minimal investment for maximum performance.

It’s a quick step that can make the difference between a limited response and one that truly delivers what you need.

Next time you work with your AI, remember: set the context, and let it perform at its best on the field of your ideas.

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