Introduction

Let’s drop the technical jargon and look at a crude reality. Imagine you hire a brand new intern, point to a computer, and scream, “Send the email!” That is a Prompt. It is a clear, actionable command. But the intern will just stare at you, terrified and confused. Why? Because they lack the Context: To whom? About what? Is the tone angry or apologetic? The debate between prompt and context engineering often misses this basic reality. You can have the most forceful, perfectly articulated command (Prompt), but without the background history and situational awareness (Context), the output isn’t just imperfect — it’s useless.

Good Vs Bad Prompt

To fix this, we need to clarify what we are actually building. A Bad Prompt is vague (“Write something about X”) and assumes the AI can read your mind. A Good Prompt is structural and psychological. It doesn’t just ask for an answer; it guides the reasoning process. We can use psychologically effective techniques like Chain of Thought (forcing the AI to “show its work” step-by-step), Collaborative Prompting (asking the AI to interview you for missing details), or Hermeneutic Prompting (iterative cycles where the AI interprets and refines its understanding of the text). These techniques elevate the quality of the response by forcing the model to slow down and “think” before it speaks. But remember: even the best psychological tricks fail if the Context (the data you feed it) is irrelevant or messy.

The Humanized Context

We struggle with this in AI because we take it for granted in human conversation. When you talk to your spouse or a long-time colleague, you are “Context Engineering” without thinking about it. You don’t need to explain your entire backstory every time you speak because there is an implicit shared context. You say, “He did it again,” and they know exactly who “he” is and what “it” refers to. AI doesn’t have that shared history. It is a stranger. We have to treat AI interactions less like a chat with an old friend and more like briefing a highly capable stranger who just walked into the room.

The How vs The What

Our position is that the industry is wasting time arguing over which discipline matters more, when the reality is that the engine requires both firing in unison. Prompt Engineering provides the reasoning architecture — using psychological frameworks like Chain of Thought to guide the “how.” Context Engineering provides the informational substance — curating the precise environment to define the “what.” A brilliant prompt operating on bad data yields confident hallucinations; perfect data with a weak prompt yields generic noise. Real success isn’t about choosing a side; it’s about acknowledging that unless both the instruction and the environment are perfect, the output will never be truly optimum.

Try this if you still disagree

If you still think one is more important than the other, try this experiment with a colleague this week.

1. Try “All Context, No Prompt”: Walk up to them, recite five minutes of background details about a project, and then just stop talking. (Watch them wait awkwardly for the “ask”).

2. Try “All Prompt, No Context”: Walk up to a different colleague and confidently say, “Please optimize the final output by EOD,” without telling them which output or what the constraints are. (Watch the panic set in).

You’ll quickly realize that in human conversation, we instinctively balance both. Why should our AI interactions be any different?