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@tjrobertson52

2025-07-04

Context engineering is the AI skill that actually matters 🧠 Better than prompt engineering? #ContextEngineering #AI #ChatGPT #Productivity #TechTips

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So most people have heard of prompt engineering by now, but there's a new term everyone's talking about context engineering. And so I wanna talk about that. If you don't know, prompt engineering is just the skill of writing effective prompts in ChatGPT or other large language models.

In the early days of ChatGPT, there's a lot of skepticism about whether or not this was really a skill. At this point, I think anyone who spent at least 1,000 hours talking to an L L m will agree not only is prompt engineering a skill, there's actually a very high skill cap. So what is context engineering?

Well, as you might have guessed, this is just the skill of being able to provide all the context necessary for the L L m to perform the specific task. My understanding is that the CEO of Shopify is the one that coined this term, but I think the reason we didn't have a name for this until recently is because only recently have L L m's been able to handle a large amount of context. A year ago, if you gave an L m more than 1,000 words, it would just end up diluting the most important parts.

But nowadays, it's pretty remarkable how good it is at following large amounts of context. Dozens and dozens of pages, tens of thousands of words. I've talked about all this before, but the point I want To make is that context engineering is a real skill, but it's not an entirely new skill.

As with prompt engineering, context engineering is about the same as it is when you're training a human to do a test. If you're bad at communicating specific goals and instructions to other humans, you're probably also gonna be bad at working with LMS. My point of view, the better I've gotten at working with LMS, the better I've become at working with humans.

I think we have this natural tendency to just be very lazy when we're given instructions. This makes sense from a biological point of view, right? We never want to expend more energy than we need to.

What I found, whether you're working with a human or a large language model, is that it's worth the five to 15 minutes it takes to provide all the instruction and context they'll need to do the task well. And if this is a task that you plan on doing multiple times, it might well be worth it to spend 30 minutes, an hour, or even an entire day putting together very specific instructions, context, and examples for the lolm or human.

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Context engineering is described as giving an LLM the context needed to complete a task as newer models handle larger context windows.

Context engineering / AI operations · asserts

  • Create reusable context packs with goals, constraints, examples, brand knowledge, source documents, and review criteria for repeated tasks.

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What is this source mainly about?

Context engineering is described as giving an LLM the context needed to complete a task as newer models handle larger context windows.

What should an operator take from it?

Create reusable context packs with goals, constraints, examples, brand knowledge, source documents, and review criteria for repeated tasks.

Which topics does it connect to?

This source is connected to Context engineering / AI operations.

What public evidence supports the record?

So most people have heard of prompt engineering by now, but there's a new term everyone's talking about context engineering. And so I wanna talk about that. If you don't know, prompt engineering is just the skill of writing effective prompts in ChatGPT or other large language models...