From prompt engineering to context engineering
The term ‘Context Engineer’ has started popping up more frequently as the hot new title in the AI space. A few repositories have appeared, posts are being written to explain the term, and like many of you, I jumped in eager to learn the next big thing. My initial reaction? This doesn’t feel new at all—it just feels rebranded. But after thinking it over, I believe it’s a needed improvement for clarity.
What Is Context Engineering?
I’m going to paraphrase a bit, but this is the best definition I’ve seen:
Context engineering involves building comprehensive, dynamic systems—using prompts, examples, memory, retrieval, tools, state management, and control flow—to deliver the right information and functionality in the optimal format, ensuring an LLM can consistently understand context and reliably accomplish its task.
Breaking this down, the key takeaway is that prompting is officially recognized as just one part of a larger system. If you’ve worked with LLMs long enough, you know a single prompt only gets you so far. The real magic happens when you build robust systems around the model—systems that keep your LLM on the rails and consistently solving the right problems.
‘Optimal format’ is the other key phrase here. During research and development, it’s fine to be a bit wasteful. But as you scale, you need systems that are tuned for consistency and efficiency, so you don’t end up spending a fortune on inference costs and content generation.
Reframing the Perspective
From my perspective, there are a couple of great reasons to embrace this term.
First, we need to update the job title to better match what people are actually doing. This field isn’t just about writing clever prompts, just like programming isn’t about writing one wickedly amazing function. There’s a much larger engineering discipline involved.
Second, ‘vibe coding,’ while fun to say, is vague. It’s not a term that inspires confidence when you’re building systems people depend on. ‘Context Engineer’ helps rebrand the work as the serious discipline it is.
But What Actually Matters
While it’s great to have a new title that people can rally behind, the fundamental building blocks we’re using remain the same. What’s valuable about this ‘rebranding’ is that it gives us a chance to bring different concepts—like retrieval-augmented generation (RAG), tool use, and state management—under one roof to push the technology forward together.
With that said, my recommendation is simple: if you’re interested in this work and haven’t started, don’t worry about your title. Just go out and start building. I’ll link the resources I’ve been reviewing below for anyone who wants to join in on the learning.