Context Engineering for Humans

AI is the amalgamation of everything humans have ever written down on computers—plus billions of dollars of synthetic data created by experts, and billions more in hardware to process it. To access nearly all human knowledge, all we have to do is ask. Well… almost. We also have to know what we want and ask with the right context.

Whether you’re prompting a large language model or asking a colleague for help, the quality of the context you provide is often the difference between getting what you want—or not. In other words, context engineering isn’t just for LLMs. It’s unreasonably effective with humans too.

Context Engineering for AI

Before we switch to humans, let’s briefly talk about AI.

AI models have a context window—a finite amount of information they can process at once. Context engineering is the art of filling that window with exactly the right information to reliably get the outcome you want. One way to define it is:

Optimizing the utility of tokens (what you put in) and available resources (e.g., tools), against the inherent constraints of language models, to consistently achieve a desired outcome.

The better the context you provide, the better the results you get back. It really is that simple.

Humans are the same—just with a few extra wrinkles.

Context Engineering for Humans

Humans have context windows too. We have limited working memory. Limited attention. Limited patience for ambiguity. When someone asks us for help, the quality of information they provide largely determines whether we want to—and whether we can—help them effectively.

Asking for help, delegating work, and leading teams all rely on the same core skill: context engineering.

Nearly everything you learn prompting LLMs applies to humans as well, with a few important differences:

• Humans must know “why.” Purpose matters. It drives motivation and engagement. This is the key difference. LLMs don’t care why; people do.
• Humans have a blind spot when providing context to other humans. We suffer from the illusion of transparency—we assume our needs are obvious. So we wait for others to notice, or we ask vaguely and expect them to “figure it out.” With LLMs, we know they can’t read our minds, so we’re far more explicit.
• Humans want to know they made a difference. LLMs don’t care whether they helped you. Humans do. Closing the loop with feedback—“Your advice helped me achieve X”—is powerful and motivating.

Making Context Simple (and Actionable) for Humans

Context engineering for humans doesn’t have to be complicated. In a humorous TED Talk, Barbara Sher says her life-changing secret to success comes down to two things:

1. State what you want (a wish)
2. State why you can’t have it (an obstacle)

As she puts it: “If you don’t say both those things, nothing happens.”

If someone says, “I’d love to be a ballerina,” people just nod politely. But if they say, “I’d love to be a ballerina, but I’m 44,” something changes. Suddenly people start brainstorming: a dance troupe for adults, a local program, a class they heard about.

Why? Because now people know exactly how to help. Once the obstacle is clear, our natural problem-solving instincts kick in.

Dr. Heidi Grant’s TED Talk expands on this by explaining the human nuances of how to ask for help effectively.

Her key points:

1. Be ridiculously specific about what you want and why you want it. Vague requests produce vague results—and no one wants to give “bad” help.
2. Skip the apologies, disclaimers, and bribes. “Sorry to bother you…” just makes people uncomfortable.
3. Ask directly. In person or on a call beats email or text, where it’s easy to say no.
4. Follow up with feedback. Let people know their help mattered.

The first point is where most people fail—with both humans and LLMs. They externalize problems before doing the work to make them solvable. They ask:

• “Does anyone know about contracts?”
• “Anyone know how to update the dashboard?”

But they haven’t clarified the problem for themselves yet. Until you do that work, your problem isn’t ready to be solved. It’s still vague, ambiguous, or underspecified.

Let's look at two improved versions of the above questions:

❌ "Does anyone know about contracts?"
✅ "I need a lawyer who handles tech startup contracts in California—I'm signing my first SaaS agreement and don't understand the IP clauses, but my budget is under $2K. Does anyone have a referral?"

❌ "Anyone know how to update the dashboard?"
✅ "I need to update our customer dashboard by EOD—I've never touched the API and the docs aren't clear on authentication. @ProjectOwner, can you point me to the right setup guide or hop on a quick call?"

The second versions work because they include:

1. The specific wish (what you're trying to accomplish) / Clear ask (what type of help you need)
2. Relevant constraints (budget, timeline, expertise level) / The exact obstacle (what's blocking you)

Leadership Is Mostly Context Transfer

Leadership is deciding what to ask for—and asking the right people in the right way.

Some people think asking for help is weakness. In reality, it’s strength. It’s also one of the most effective forms of delegation. And delegation fails far more often from poor context than from incapable people.

Ambiguity doesn’t delegate well. Great leaders and great prompt engineers do the same thing: they transfer responsibility by transferring context.

At first, delegation feels inefficient. You know you could do it faster and better yourself. The challenge isn’t seeing the outcome—it’s translating that clarity into something someone else can execute.

Delegation breaks down when you haven’t clearly explained:

• Why the work matters
• What success actually looks like
• What constraints exist (time, money, quality, risk, politics)

Then comes the hardest part: patience. You have to let others find their own path—even if it’s imperfect. Many leaders give up here and swoop in to “fix” things. Great leaders resist that urge. They understand that context transfer is a process, not an event. It’s how capable, independent teams are built.

Summary

Context engineering isn’t just for AI. It’s a life skill that works exceptionally well with humans too.

Key takeaways:

1. AI can’t wish your wishes for you—and neither can your team. You have to know what you want.
2. Humans have blind spots when asking for help. Be explicit, skip the apologies, and close the loop with feedback.
3. Whether you’re talking to silicon or people, context is everything. Clear, specific, purpose-driven context is how you get better results—consistently.