AI
Prompt Engineering Matters. But Context Engineering Is Where the Real Multiplier Lives.
Prompt Engineering Matters. But Context Engineering Is Where the Real Multiplier Lives.
Many people working with AI think the main job is writing prompts.
My experience has been a bit different:
The critical work starts before the prompt.
It starts with building context.
Sometimes I spend hours building context before giving a single task to AI.
But this is not abstract time spent "thinking about prompts."
On the contrary, it is usually the most hands-on part of the work. In many cases, it is the part where I feel I am most truly working.
What That Process Looks Like
For example, if I am working on a software project, this is usually what that process looks like:
I clarify the problem. I define the scope of the work, what I am trying to solve, why I am solving it, and how it could be solved. In many cases, this is a highly interactive process where I actively debate ideas with AI. And honestly, this is something I have been feeling very strongly lately: some of the intellectual discussions I have with Claude Opus are discussions I simply cannot have with most people around me. In my previous post, I talked about a 10x multiplier effect. I think this is exactly where it shows up. The higher a person's potential, the more AI can help unlock and elevate it.
I define the work at the FSD and TSD level. This part takes serious time. But with a strong FSD/TSD, you can build an agent team that can code autonomously for 8-10 hours without interruption. In many cases, that represents a level of output that would have taken months in the pre-AI era.
I build the agent structure in Claude Code. For example: 1 PM, 1 solution architect, 3 developers, 1 code reviewer, and a tester.
I break the larger goal into smaller tasks using sprint logic. Here I usually use Vibe Kanban MCP, and I grant access to that MCP to the PM and SA roles.
I define what each role is responsible for.
I design the review and feedback loop.
I clarify the acceptance criteria and define what "good output" actually means.
If you are building a project from scratch, this setup really can code independently for 8-10 hours straight.
Of course, I do not use the same approach for every project.
But in many cases, I end up spending almost as much time preparing the system as the agent structure will later spend producing output.
And honestly, the result is usually more than worth it.
Beyond Software
And this does not apply only to software.
If I am working on a marketing project, context engineering includes things like: competitor analysis, turning everything in my head into written brainstorming, collecting reference work, organizing the data I already have, and presenting it to the model in the right format.
In other words, the job is not just asking AI for something.
It is more like building a small digital team and designing in advance how that team will work and what information it will have access to.
The Filmmaking Analogy
If I had to explain it with an analogy:
You do not start a film by just saying, "Start shooting."
First, the script is clarified. Roles are assigned. The scene flow is planned. Everyone knows what they are responsible for. Success criteria are defined.
A prompt sometimes feels like the director saying "action."
But what makes a great scene possible is the entire system built before that word is spoken.
I think the same is true for AI:
In many cases, quality does not come from the command itself.
It comes from the structure that exists before execution begins.
The Real Multiplier
Most of the time, I do not think of AI systems as a single assistant.
I think of them as a well-structured product-engineering organization.
Of course, the prompt matters.
But in many cases, what determines the result is not the prompt itself.
It is the system the prompt sits inside.
That is why I think prompt engineering is a useful skill.
But the real multiplier effect often comes from context engineering.
Because well-prepared context leads to:
- Less repetition
- Less drift
- More consistent decisions
- More usable output
- Faster review cycles
In Short
What you ask AI matters.
But how you design the environment it works inside matters just as much.
I believe one of the most valuable skills in the coming years will be this:
Not just writing good prompts, but building the right working context.
Hayreddin Tüzel
CTO & Co-Founder @ Flalingo
Best online English learning platform