The Codebase Is the Prompt

DEV Community

Joined

Dec 29, 2025

I think this is exactly the interesting crack in the argument.

The codebase can tell you what it currently does. It cannot reliably tell you what it was supposed to do, why it became that way, which constraints are accidental, which are sacred, and which "temporary workaround" from 2019 is now load-bearing infrastructure with a mortgage and two children.

That missing information is exogenous to the codebase. And I think that is the part where the human still matters a lot.

If the human developer has no more context than the model — no product history, no company intuition, no memory of past incidents, no sense of which customers scream the loudest, no knowledge of what the team values, no understanding of which other systems depend on this one — then yes, the human may just be compressing the same chaos into a more confident-looking shape. At that point the loop is not very magical. It is just two entities hallucinating in sequence, one of them with coffee.

But in practice, humans often do bring more than the code.

They have seen how the company works. They know which tradeoffs are usually accepted and which ones get rejected in review. They know that "clean architecture" is nice, but shipping the billing fix before Friday is nicer. They have seen neighboring projects. They know which patterns survived contact with production and which ones only looked good in the architecture diagram. They have intuition about where risk hides in this specific organization.

That context is not in the repository. It is not in the README. It is often not written down anywhere, because apparently we as an industry decided that the most important knowledge should live in Slack threads, incident memories, and the head of someone currently on vacation.

So I agree with the challenge: "have a knowledgeable developer compress the chaos for the model" only helps if the developer is injecting constraints that the model could not infer from the code alone.

But I think that is exactly the sweet spot.

The human is not valuable merely because they can read the same files more slowly. The human is valuable because they can add the missing outside context: intent, priority, history, risk, taste, politics, customer pain, operational scars, and all the other deeply unglamorous information that determines whether a change is actually good.

The hard part is that making this context available to the model is a huge undertaking. Most companies do not know how to do it yet. Many do not even know how to make it available to their own developers. The model exposes the same organizational problem, just with better autocomplete.

So maybe the practical version is:

LLMs are very good at working inside a compressed frame. Humans are still needed to choose and correct the frame.

The mistake is expecting the model to derive company intent from code alone. The opportunity is using humans to supply the exogenous constraints, then letting the model operate within them.

Not "AI replaces the developer."

Not "the developer babysits the AI."

More like: the developer turns organizational fog into a sharp enough query that the model can actually be useful.

Which is less glamorous than the marketing pitch, but probably closer to the real leverage.

jugeni profile image

Mike Czerwinski

Building jugeni. LLM mechanics · forensic watermarking · operator discipline · Polish-tech multidomain.

Location

Poland
Pronouns

Pronounced ju-geh-nee.
Joined

Jun 19, 2026

• Jun 28

You moved the crack to exactly the right place and then stopped one step short of it. Exogenous-at-input is not the same as exogenous-at-check. A human who turns the org fog into a sharp query has supplied a frame, but that frame is now consumed by the same loop that has to grade the result, and the only test left is whether the output is consistent with the frame the human authored. That is your two-entities-hallucinating-in-sequence problem again, just relocated: the human compresses the company into a constraint, the model executes inside it, and both agree because nothing outside the loop ever weighed in. The intent in the prompt has no counterparty until production hands it one. What makes the operational scars actually exogenous is not that they live in someone's head, it is that they have a payer: the incident that fires, the customer who screams, the neighboring team that vetoes in review. Those can contradict the output after the fact. A constraint supplied at write-time and never able to overrule the plausible result is indistinguishable from a confident guess in nicer words. So the human's load-bearing role is not supplying the frame, it is keeping the standing to reject what the frame produced against context the loop never saw. The frame is the cheap half. The veto is the one with a mortgage and two children.

timon_krebs_c89f82a68ba4c profile image

Timon Krebs

Joined

Dec 29, 2025

• Jun 28

Yes, that is a very fair push.

I do not think it replaces the previous point, though. It completes it. Apparently the human does not have one job in this loop. Of course not. Software would never be that kind.

There are really two human jobs here.

First, the human helps shape the frame: product history, company values, operational scars, weird customer constraints, team preferences, and all the context that is not sitting politely in the repository waiting to be embedded.

But second, the human keeps the right to veto.

Because a good frame is still only an input. It can make the model more useful, but it does not make the output true. The model can satisfy the prompt beautifully and still be wrong in the world, which is basically software engineering wearing a small hat.

So yes: exogenous-at-input is not enough. The loop also needs something exogenous-at-check.

And interestingly, the check side may be easier to partially automate than the frame side.

We already have tools for letting reality vote: tests, type systems, linters, code review, A/B tests, canary releases, feature flags, metrics, alerts, incident reports, customer feedback, rollback buttons, and the ancient enterprise ritual of "wait, why is conversion down?"

Those mechanisms are imperfect, but they have one huge advantage: they can contradict us.

A prompt cannot scream. Production can.

That makes checks special. They are not just more context inside the same hallucination soup. They are contact with the outside world. They can say: "Nice theory. Unfortunately, latency tripled and the billing team is now holding a tiny pitchfork."

The frame tells the model what kind of solution to attempt.

The check tells us whether that solution survives contact with reality.

Both matter, but they are not equally easy to systematize. Framing requires translating messy organizational knowledge into useful constraints. That is hard because most companies barely manage to transmit this knowledge to humans, let alone to a model. It lives in Slack archaeology, incident folklore, customer scars, and the head of someone currently on vacation in Portugal.

Checks, by contrast, already have infrastructure. We know how to automate parts of them. We know how to run experiments, compare metrics, gate rollouts, detect regressions, and roll back bad changes. We do not need the model to perfectly understand the company if we have systems that can safely tell us when its output is nonsense.

So maybe the sharper version is:

The human’s value is not only compressing chaos into a prompt. It is also deciding which parts of reality get a veto.

And that is where experience becomes more valuable, not less. Experienced developers are better at framing the problem, but also better at knowing what must be checked before trusting the answer.

The model can work inside the frame.

The checks make sure the frame did not accidentally become a very confident bedtime story.

jugeni profile image

Mike Czerwinski

Building jugeni. LLM mechanics · forensic watermarking · operator discipline · Polish-tech multidomain.

Location

Poland
Pronouns

Pronounced ju-geh-nee.
Joined

Jun 19, 2026

• Jun 28

You completed it cleanly, and there is one more turn left in the check half. Your list of checks is only exogenous where the oracle has a payer the loop did not appoint. A test the same agent wrote is the frame again in assertion form. It passes because it encodes what the author already believed, and it goes green on exactly the case nobody thought to doubt. Type systems, production metrics, incident reports earn their veto from a source outside the change: the type checker was authored by someone who never saw your feature, production by users who never read your prompt. That is what lets them contradict you. The trap is the check that looks exogenous and is not. The eval the model wrote for itself. The assertion lifted from the same spec the code was built against. It runs forever at zero tokens and confirms the author to the author. So the check side is easier to automate, but only the part that was already external. The unautomatable half is deciding which checks have standing to overrule the result, and which are just the frame in a lab coat.

timon_krebs_c89f82a68ba4c profile image

Timon Krebs

Joined

Dec 29, 2025

• Jun 28

This article is not the full answer. It is the first constraint.

If the codebase is the prompt, then bad code gives the model a bad query. That is the point I want to land here.

The check question is the sequel.

And even there, tests are not "just checks." They are also documentation, intent, and trace. Even generated tests have value because they show what the model thought mattered. They expose the shape of its interpretation. That makes them useful not only for validation, but also for review: "Ah, so this is the part of the problem the tiny silicon intern was staring at."

But that belongs more to the quality argument: why checks, traces, tests, and feedback loops are not process overhead, but the infrastructure that lets you move faster without turning your codebase into a haunted escape room.

And then there is the third article: agents as actual outside pressure. Not just "the model wrote code and then nodded approvingly at its own homework," but autonomous systems that inspect, run, compare, challenge, and validate from another angle. That is where my agent harness comes in — and I am already getting real value out of it!

jugeni profile image

Mike Czerwinski

Building jugeni. LLM mechanics · forensic watermarking · operator discipline · Polish-tech multidomain.

Location

Poland
Pronouns

Pronounced ju-geh-nee.
Joined

Jun 19, 2026

• Jun 28

The three-article split sounds right, and the second one is the half that is harder to write because the temptation is to keep extending the first. "The codebase is the prompt" is a strong-enough first constraint that you can spend a whole post just on its implications, and the check piece always feels like a smaller sequel even though it is actually doing more work.

One flag for the third article: tests-as-evidence has a quiet failure mode when the test author and the implementation author are the same model in the same window. The tests document the model's interpretation, which is what you said, but that is not the same as documenting whether the interpretation was right. The trace shows you what the silicon intern thought mattered. It does not show you whether they thought the right thing. That is where the outside-pressure harness earns its keep, because the gap between "what I checked" and "what I should have checked" is the part the same window cannot see.

Looking forward to the harness piece. The fact that you are already getting real value out of it means the third article will have receipts the first two had to gesture at.

nazar_boyko profile image

Nazar Boyko

Software engineer, backend & AI-focused. Node.js, TypeScript, Go, PHP/Laravel, AWS. I write a lot about reliable systems and AI agents that actually ship. More at nazarboyko.com

Email

boyko.nazar@gmail.com
Location

Austin, TX
Education

M.S. Computer Science
Joined

Aug 2, 2024

• Jun 29

Calling a bad codebase a bad prompt instead of a hard query is what makes this click for me. It quietly changes what the work even is. The job stops being how you word the request and becomes where you cut the task so locality still holds, and only someone who already knows the system can cut in the right place. The honest catch is the one you raised yourself, that nobody always knows which far-off file holds the real context, so half the work is finding that boundary before you can even shrink down to it.

timon_krebs_c89f82a68ba4c profile image

Timon Krebs

Joined

Dec 29, 2025

• Jun 29

Sometimes half the work is archaeology before engineering even begins. In legacy codebases this seems to matter even more now.

So maybe the practical rule is:

Do not just prompt the model to solve the problem. First use the model, and your own system knowledge, to find the smallest slice of reality where the problem becomes locally understandable.