Prompt Engineering vs Context Engineering: What a Business Owner Needs to Know

Prompt engineering is how you word your request to an AI. Context engineering is what the AI knows about your business when you ask. When an answer comes back weak, check what the AI was given first, and add what it was missing.

Say you ask the AI to write a follow-up email to a client who went quiet. What comes back could have come from any company. So you add “make it warmer,” try again, and get a slightly different version of the same thing.

What is the difference between prompt engineering and context engineering?

Prompt engineering is the wording of your request, and context engineering is everything else the AI has available to work from when it answers. Say you type, “Write a follow-up email to a client who went quiet.” That is a prompt. Now add the last three emails in the thread, a line saying this client dislikes being pushed, and a note you sent last month that you liked. That is context. The request is the same one-liner, but the AI now has your situation to work from instead of guessing at it, so the draft that comes back is more likely to be one you’d send.

Why doesn’t a better prompt always fix a weak answer?

Rewording changes how a request reads but adds nothing about your business, so it can only go so far.

People still ask me how to prompt it and how to ask the question. On Coffee Brief Episode 2, talking with John Matoso, I said my son would put it bluntly: bro, no. You don’t have to think about prompting anymore. What matters is the context and the goal. Has the AI been given enough to understand what you are really trying to do? If it hasn’t, you don’t get quality out of it.

I also said in that conversation that today’s models are smart enough to read between the lines about what you’re trying to get at.

Careful wording isn’t a waste of time, and a clear request still helps. It works better once the AI has something to work from.

What counts as context in a small or medium business?

Context is the information about your business that someone would need before doing the task well. For small and medium businesses, it usually comes from five places:

• Who the work is for: your customer, what they care about, how they like to be spoken to.
• What good looks like: one example of your best work, such as a proposal or email you’re proud of.
• What already happened: the last email thread, the notes from the call, the order history.
• Your real numbers: actual turnaround times, volumes or constraints, so the AI doesn’t invent them.
• Your rules: words you never use, promises you never make, how formal you are.

Hand the AI the details it needs for this request.

One caution before you paste. This is general advice, not something from the episode: leave client details and other confidential information out until you’ve checked what your AI plan and settings do with it. Unmanaged use is the risk covered in Shadow AI in a Small Business With No IT Department: What to Do This Week.

What should you do before you type?

Before you type, ask two questions: what are you trying to achieve, and have you given the AI enough to know that?

1. What am I actually trying to achieve? “Write an email” is a task. “Get this client to reply this week without feeling pushed” is a goal.
2. Have I given it enough to understand that? If you’d have to explain the situation to a person before they could start, put that explanation in the message.

If the first answer is generic, check which of the five items above you left out, and add it.

Is context engineering a developer thing?

In its technical sense, yes. Developers use “context engineering” for deciding what information an AI product is fed at each step.

You meet the owner’s version whenever you give the AI your files, paste in your notes, upload a past proposal, or save standing instructions about your business so you don’t retype them. It is the same idea at a smaller scale, with no code. You decide what the AI should know.

Here is my view, and it is a view, not research. Giving an AI context is a skill everyone should learn, and most business people and managers already have it in a different setting. When you align a team, you explain what you’re doing and why, so everyone can reach the goal of the project. Briefing an AI is that same move.

What is one thing to do this week?

Write plain-text answers to seven questions, a sentence or two each. The first five are about your company, so you answer them once. The last two you answer fresh for each request.

1. Who are your customers, and what do they care about?
2. What do you sell, and what do you not sell?
3. What does one example of your best work look like?
4. What do you never say or promise, and how formal are you?
5. How does a typical job run from start to finish?
6. What are you trying to get done with this task, and who is it for?
7. What has already happened on this one?

Paste your answers above your next three AI requests, and compare what comes back with what you got before. Which jobs to hand to AI at all is a separate question, and the automate-or-augment test for one job at a time covers it.

To hear the conversation behind this article, listen to Coffee Brief Episode 2.