Recently, I see the term Context Engineering much more often than before.
I think that’s a good thing.
People are starting to realize that building AI applications is not only about choosing a model or writing a prompt.
Context matters.
But the more I think about it, the more I feel there is another question we should ask.
How should we engineer context?
To me, this is not very different from software engineering.
Good context doesn’t happen by accident
Useful context rarely appears naturally.
Someone has to discover it.
Someone has to write it down.
Someone has to review it.
Someone has to improve it.
Over time, people learn better ways to explain things.
Requirements change.
Projects evolve.
Teams grow.
Context changes just like software does.
Knowledge needs maintenance
One thing I’ve learned from software development is that code is never finished.
Knowledge isn’t either.
Documentation becomes outdated.
Best practices change.
Architecture evolves.
If context becomes an important part of AI systems, then maintaining context becomes an engineering problem.
Not a writing problem.
Small pieces are easier to improve
One idea that strongly influenced Renma is keeping context small.
Instead of one huge document, I prefer many focused context assets.
Each asset should explain one thing well.
Small assets are easier to:
- review
- update
- combine
- reuse
This feels surprisingly similar to software design.
We split large functions into smaller ones.
We separate responsibilities.
Maybe context should follow similar principles.
Relationships matter
Knowledge rarely exists in isolation.
A testing guideline may depend on an architecture document.
A coding convention may refer to a security policy.
A review checklist may require project-specific terminology.
These relationships are important.
Without them, context becomes a collection of disconnected documents.
Software engineering has dependency graphs.
I think context deserves something similar.
Review matters more than generation
Today’s LLMs are already very good at generating documents.
Creating context is becoming easier every year.
Reviewing context is still difficult.
Is this still correct?
Does this conflict with another document?
Is something missing?
Has this become outdated?
These questions matter much more than simply generating another page of text.
Traceability builds trust
When someone asks,
“Why does this context exist?”
I want to have an answer.
Maybe it came from a design document.
Maybe it references a GitHub issue.
Maybe it reflects a production incident.
Maybe it summarizes years of team experience.
Good context should have traceability.
Not because LLMs need it.
Because people do.
Trust comes from understanding where knowledge comes from.
Models will change
The AI world moves incredibly fast.
Models improve every few months.
Tools appear and disappear.
Agent frameworks evolve.
I don’t expect today’s workflows to remain the same.
What I hope will remain valuable is well-managed knowledge.
If we invest in organizing, reviewing, and improving context today, I believe that investment will continue to pay off, even as the AI ecosystem changes.
Maybe that’s what Context Engineering really means to me.
Not writing better prompts.
But applying software engineering principles to knowledge itself.
~ my tech diary ~ 