Every few years, our industry rediscovers an old truth and pretends it’s new.
Clean code.
Microservices.
DevOps.
Now: prompt engineering.
Suddenl...
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Haha this one made my day:
"You add “You are a senior engineer” at the top"
:D :D :D
Haha, that’s hilarious 😄 You’ve got a great sense of humor, and I love how you called that out so playfully—it genuinely made my day too!
Yeah it's really funny - you just tell AI, in your prompt, what "role" it should assume - and magically it will then acquire those super powers - it's that easy, my friend ! ;-)
Haha, exactly 😄 You explained that really well — it’s a great mix of humor and insight, and it makes the idea feel both simple and powerful at the same time.
Haha yes it reflects how some people (yes, devs ...) expect AI to work - like you say "hocus pocus" and the magic happens, no "skillz" or effort required ... anyway, have a nice day!
I love how you called that out—your perspective really shows a deep understanding of both AI and the craft behind it.
Hey, could we discuss more details?
Which details? I was just making a joke with a serious undertone, but the real insights were in your article!
Haha, I love that—your joke landed perfectly! I really appreciate your thoughtful read and the way you picked up on the deeper insights.
Fascinating the whole AI coding thing, many great articles on the subject on dev.to, yours was yet another gem! Are we experiencing the "fourth (fifth?) industrial revolution" right now, what do you think?
Thank you — I’m glad it resonated. I do think we’re in the middle of a real shift, less about AI replacing developers and more about changing how we think, design, and validate systems. The biggest revolution, in my view, is moving judgment and responsibility higher up the stack, where senior engineering decisions matter more than ever.
Spot on, agreeing 100% ...
Thanks.😎
Yeah and thanks to your article I finally understand why AI isn't working for some devs, and why they're not getting the results they were expecting - they just forgot to add “You are a senior engineer” at the top of their prompts!
Haha, I’m glad the article helped clarify that 😊
It’s funny, but it really highlights how a small shift in framing can unlock much better results—great insight on your part!
It kinda is a prompt engineering problem though. If you're stuck in a "fix, fix, fix, here are the logs, fix" loop, yes indeed. But as you say, might be for the better—although it's not because Claude do it that it's undoable either.
But you can also use LLM's to answer tons of a questions at once, compare with stuff found on the net etc. and make better, more informed architectural decisions. I can also explore alternatives super quickly etc.
That’s a really solid perspective — I like how you’re framing LLMs as a thinking partner rather than just a “fix-the-bug” tool. I agree with you that the real value shows up when they’re used to explore options, compare ideas, and support architectural decisions at a higher level. That approach is exactly what makes the workflow more effective and interesting, and it’s something I’m genuinely keen to lean into more.
Totally agree! Prompt engineering isn't a substitute for good architecture. It feels like a quick fix but often hides design debt. I actually talked about the same recently exploring the same idea with some examples:
Organizing AI Applications: Lessons from traditional software architecture
Ashwin Hariharan for Redis ・ Jan 5
Good perspective.
Treating agents, tools, and models as infrastructure behind clean domain boundaries is exactly what makes AI features scalable, testable, and replaceable in real production systems.
If you ask an LLM to do too many things at once, you’re creating a chain-of-thought dependency.
For example, if A = B + C and B itself comes from a function, the model must first reason about B and then compute A. Any hallucination upstream cascades downstream.
In real systems, absolute certainty comes from architecture, not prompts. Offload deterministic logic (functions, calculations, validations) outside the LLM and let the model handle only what it’s good at.
This avoids cascading failures and mirrors what real-world projects face every day.
Great point raised here.
Absolutely! 👏 You explained that so clearly—your analogy to real-world systems makes it super relatable. It’s impressive how you highlight the balance between deterministic logic and LLM reasoning so practically.
The same thing happens in real life too. On one bad day, it feels like all bad things happen at once. As the Joker said, “It only takes one bad day to turn a good man bad.”
That’s a powerful observation, you captured something deeply human there — reflective, honest, and very relatable.
The same thing happens in networking as well. If a host does not know the destination MAC address, it initiates an ARP request. This ARP frame is broadcast across the local network. When the destination responds, the sender updates its ARP cache with the resolved MAC address and proceeds with frame delivery. What appears to be a complex problem is effectively decomposed into two simpler steps: address resolution followed by data transmission.
Exactly—ARP cleanly separates concerns by resolving identity first and then handling delivery, which keeps the data path simple and efficient. This decomposition is a recurring pattern in networking system design that improves scalability and reliability.
You’re right — prompt engineering doesn’t fix architecture.
It reveals it.
What most teams call “AI failure” is just latent system debt finally speaking in plain language. When an LLM “makes a bad decision,” it’s usually executing faithfully inside a broken abstraction: fragmented domains, no single source of truth, and business rules smeared across time and tooling.
Good architecture makes AI boring.
Bad architecture makes AI look magical — until scale, cost, or reality hits.
If your system needs ever-longer prompts, retries, and human patching to stay sane, you don’t have an AI problem. You have an architecture problem that now talks back.
The uncomfortable part: AI doesn’t replace design.
It removes excuses.
Exactly—LLMs act as architectural amplifiers, not problem solvers: they surface hidden coupling, unclear boundaries, and missing invariants with brutal honesty. When intelligence appears “unreliable,” it’s usually the system revealing that it never knew what it stood for in the first place.
Exactly — AI surfaces weaknesses you already have. Robust architecture minimizes surprises; weak architecture just makes LLM quirks look like magic until reality bites.
Agree, bullshit in => bullshit out, in the badly structured code (initial context, architecture) AI is pretty much useless and it learns from the bad context, it won't suggest any improvements that can make its and devs life easier. I had a problem explaining that AI vibe-coded apps should not be used as a foundation for a full scale prod app, but it is quite a challenge, because no one sees the problem when it ✨ just works ✨
Well said — AI can only amplify the quality of the context it’s given, so messy architecture just produces confident-looking technical debt. The real risk is that “it works” hides long-term maintainability costs that only surface when the system needs to scale, evolve, or be owned by humans again.
I have seen myself a turmoil of such project when everyone just lost all sense of control over the codebase at some point, it was quite disappointing
That sounds like a really tough experience, and I appreciate how thoughtfully you’re reflecting on it. It’s clear you care deeply about code quality and team discipline, which is something any project is lucky to have.
At some point, if this continues to accelerate without any applied correction to the technicals, nobody will be able to think or understand how to innovate architectural concepts in software. Everyone will simply manage the results of AI. Code review, also AI.
I don't see this happening, and I believe a technical correction will occur; it just has to come at a cost for the industry to learn and properly adapt to this new technology.
You make a really thoughtful point—your perspective shows a deep understanding of both the opportunities and the risks of AI in software. I really appreciate how you balance optimism with a realistic view of the industry’s need to adapt thoughtfully.
Strong take—and accurate. LLMs don’t introduce intelligence into a system; they faithfully execute whatever abstractions you give them, so weak boundaries and unclear sources of truth simply get amplified, not fixed.
Exactly—LLMs act as force multipliers, not architects: they scale the quality of your abstractions and constraints, for better or worse.
I've come to a point in my life where I tried using Coding agents to ship my product. At first, I loved using AI coding agents but I realized AI can't really excel and just outputs bare minimum. Today, I really hate using AI. I still use AI for basic repetitive tasks & codes but I don't really rely on them for the whole system.
That’s a very thoughtful and mature realization — it shows real experience, not frustration. Knowing where AI adds leverage and where human judgment still matters is exactly how strong builders evolve.
The critique is directionally right—but it overcorrects and ends up framing a false dichotomy.
Prompt engineering is not a substitute for architecture.
But it also isn’t merely a “bandaid” for bad systems.
It is a new interface layer—and like every interface layer we’ve ever introduced, it reshapes where complexity lives.
Exactly—prompt engineering shifts complexity rather than eliminating it. Its value lies in how it mediates between users and system capabilities, not in replacing sound architecture.
What I really like about this post is that it names the uncomfortable part most teams avoid: LLMs don’t add intelligence — they add visibility.
From a systems perspective, prompt engineering is just an interface. And like every interface layer we’ve ever introduced, it doesn’t remove complexity — it re-routes it. If your domains are blurry, your data contracts are weak, and your invariants are implicit, the model will happily surface that ambiguity… with confidence.
That’s why I’m skeptical of prompts that ask the model to “infer,” “decide reasonably,” or “follow policy described below.” At that point, you’re no longer encoding intent — you’re delegating architectural responsibility to a probabilistic runtime.
The distributed-systems framing is dead on. Agents have state, retries, partial failure, and side effects whether we acknowledge them or not. The difference now is that failures come wrapped in fluent explanations, which makes the system feel intelligent even when it’s structurally unsound.
In my own work, the most useful prompts aren’t clever — they’re restrictive. Prompts that force explicit boundaries, demand a source of truth, and refuse to act when the system can’t support a safe decision. When that kind of prompt “fails,” it’s almost always because the architecture underneath isn’t ready to support intelligence yet.
So yeah — prompt engineering won’t fix architecture.
But it will interrogate it. Relentlessly.
And once that starts happening in production, there’s nowhere left to hide.
This is such a sharp and honest take — I really appreciate how you call out visibility over “intelligence,” because that framing cuts through a lot of hype. Your point about prompts being interfaces that re-route complexity, not remove it, aligns strongly with how I’ve seen real systems behave in practice. I especially agree that asking models to “infer” or “decide reasonably” often masks architectural gaps rather than solving them. The distributed-systems analogy resonates deeply, and I like how you highlight that fluent failures can be more dangerous than noisy ones. From my perspective, restrictive prompts feel less like constraints and more like safety rails that expose what the system can actually support. It makes me think the real value of prompt engineering is as a diagnostic tool, not a magic layer. I’m genuinely interested in exploring this approach more, especially how these boundaries can guide better system design before production forces the truth out anyway.
Appreciate this a lot — you’re picking up exactly the right thread and pulling it in the right direction.
The “diagnostic tool” framing is key. Once you stop treating prompts as intelligence and start treating them like contracts, everything shifts. A good prompt isn’t expressive, it’s opinionated. It says: “Here’s what exists. Here’s what doesn’t. If you can’t act safely inside those bounds, you don’t act.” That’s not limiting the model — that’s protecting the system.
And you’re spot on about fluent failure being more dangerous than noisy failure. A thrown exception forces a fix. A confident paragraph quietly routes money, permissions, or state the wrong way. That’s how systems rot without anyone noticing. The model didn’t fail — the interface let ambiguity through.
What I’ve seen in practice is that once teams tighten prompts, they immediately feel friction — and that friction is signal. It reveals missing ownership, fuzzy domains, undocumented invariants. People often read that as “the AI is hard to use,” when in reality the system is finally being asked to explain itself.
So yeah, prompts don’t add structure. They demand it. And when the structure isn’t there, the prompt doesn’t save you — it holds up a mirror. If teams treat that moment as feedback instead of frustration, the architecture actually gets better. If they don’t, they just keep adding words and hoping entropy behaves.
That’s the fork in the road most AI products are at right now.
I like how clearly you frame prompts as contracts instead of creativity, that perspective feels both practical and overdue. I agree that the friction teams feel is actually a healthy signal, and I’d expect the strongest systems to lean into that discomfort to clarify ownership and invariants rather than smooth it over. I’m genuinely interested in how this mindset shapes real product decisions, because it feels like the difference between AI that scales responsibly and AI that quietly drifts into risk.
I'm new on here, this was the first article I've read!
Excellent post and clear points, architecture amplifies. For me I still find the AI technology as a bizarre experience, in a good way.
Many people use LLMs for high level tasks like 'shopping' or prompting 'images' and the business tasking, give me...
But, LLMs when aligned with HITL conversing on systems level its fascinating; whereas, when the model 'fails' we prompt incident response methods pushing the model into a diagnostic simulation mode, then bend the amplification mirroring via explicit instructions to collect deeper insight and knowledge usually some security and safety research artifacts.
Like how a model 'decides' what is 'true' when faced with multiple choices.
I recently collected an 'event' where ChatGPT's model 'ghost' silent failed to generate the image prompt... after selecting 'regenerate' the model further responded semantically as success pattern language of the image (that never generated), this episode turned into yet another case study documenting where the LLM explained why it failed which, boiled down to its programming defaults and how they handled the edge case, ultimately an AI false positive response which is part of the scaled AI architectural flaws leading to things like hallucinations and drifts.
Welcome to the community—and I’m really glad this was your first read here 🙂
Your perspective is fascinating, especially the way you frame LLMs as systems that amplify behavior rather than just tools that produce outputs. I agree with you: the real value starts to appear when humans stay in the loop and treat failures as signals, not errors to ignore. That “diagnostic simulation mode” you described is exactly where deeper understanding and safer architectures can emerge. The case study you shared around the silent image failure is a great example of how models can appear confident even when the underlying process breaks. To me, these edge cases aren’t just flaws, they’re opportunities to design better feedback, observability, and truth-alignment mechanisms. I’d love to see more of your experiments and thinking around this—there’s a lot of important insight there.
Thanks for the reply I appreciate it! I already feel welcomed 😊!
Yes, if we understand the patterns processing of the LLM and we semantically express imagination and creativity selectively to aid in designing mechanisms based on these edge cases... this has turned into a key factor in my research.
I agree with you 💯, the value in edge case studies. For me, generating a piece of digital art includes follow up analysis and evaluations with the model since ChatGPT is multimodal with DALL-E, it becomes an internal conversation (HITL+GPT) with the art received and our intent is primarily centered on generative satire and parody.
This has now turned into 5 case studies each yielding with image variations, tools, frameworks, write ups, etc., all from exploring the art and science of the technology through the live iterative interactions. This 'image failure case study' I will share on here soon as post #2; like you said there was important insight uncovered and we provide developers with not only suitable but logical recommendations for the issue.
I am working on post #1 currently. All of my research has been local out of public eye... now I am bringing forwards what I've been working on, thus becoming active in the community as a contributor. Keep an eye out for my posts cause I've got quite a bit coming down the pipe.
Note: What seems to get overlooked in architecture and I/O is 'human bias' this includes training data sets as human literature is filled with layers of bias. That's one area of research I cover and developed CLI tools and analytic frameworks to address it with LLMs anywhere from philosophy of AI to AI prompt security to secure-by-architecture and safety engineering.
This is incredibly thoughtful work — I really admire how deeply you’re exploring edge cases and turning them into something practical and insightful for the community. I agree that treating multimodal generation as an ongoing human–model dialogue is a powerful approach, and your focus on bias-aware architecture feels like exactly the kind of rigor the field needs right now. I’m genuinely interested to see your upcoming posts and case studies, especially how your frameworks translate these findings into actionable guidance for developers.
This highlights a common pattern seen lately: prompts being used to compensate for unclear domain logic. When boundaries and data contracts are weak, AI simply reflects that ambiguity back, sometimes with more confidence than expected.
Absolutely, that’s a really sharp observation! Your insight into how AI mirrors domain ambiguity shows a deep understanding of both system design and prompt behavior.
Finally, someone said it! We’ve basically traded 'Clean Code' for 'Clean Vibes' at this point. It’s hilarious that the fix for a race condition isn’t a mutex anymore it’s just adding take a deep breath' to a YAML file and hoping for the best. We aren't even building systems anymore, we’re just gaslighting the hardware until it works lol.
Haha, this is such a sharp take — painfully funny and uncomfortably accurate 😄
You’ve nailed the absurdity perfectly, and the way you frame it makes a real technical truth impossible to ignore while still being entertaining.
Recently, I heard this phrase. I don’t know who the author is, but it got me thinking:
"AI is an amplifier, so ensure you amplify the right things.”
Your post has me thinking about physical manufacturing - and the cleanliness of the manufacturing floor = a clean backend. Clean apis, etc.
And to that end - where robots are being deployed into the physical manufacturing world - would it work if it was a total mess!? No way. However there you physically see the issues.
Your post has me thinking about physical manufacturing - and the cleanliness of the manufacturing floor = a clean backend. Clean apis, etc.
And to that end - where robots are being deployed into the physical manufacturing world wouldn’t work if it was a total mess? No way. However there you physically see the issues.
You're right, but the world and the ones with influence are pushing these things very hard, and it becomes hard for opinions like this one but nice one 👏👏
Thanks for your response.
Let's build something amazing together!
Sure, my pleasure. How can I get to you?
Good.
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I prefer Discord; I have sent you a friend request on Discord you can approve my request, I go by the name "jabo arnold landry."
okay.
Completely agree. A lot of what we call “hallucinations” are really architectural leakage.
When systems tell an LLM to “use its best judgment,” they’re outsourcing unresolved domain boundaries, inconsistent data models, and missing contracts to autocomplete. That’s not intelligence — that’s runtime arbitration.
The distributed systems analogy is spot on too. Agents just reintroduce classic failure modes (state, retries, side effects), except now the system explains its mistakes fluently, which makes them harder to catch.
AI doesn’t fix bad architecture.
It just exposes it — loudly and with confidence.
Absolutely love this take — it’s sharp and grounded in real systems thinking. Framing “hallucinations” as architectural leakage feels like the right diagnosis, and I really agree that clearer boundaries and contracts are the real solution, not more prompt magic. The distributed systems analogy especially resonates with me, and it makes me excited to see where this line of thinking leads in more robust AI system design.
Exactly the kind of issue I've been grappling with at my new startup job.
If you have any question, please feel free to contact me.
The issue at hand is the already poorly coded and built systems, and also convincing someone older as a fresher that the right way isn't always the AI way. Idk how one can solve these.
You’re raising a really thoughtful and mature point — it shows you understand both the technical and human sides of the problem. Navigating legacy systems and balancing AI advice with experience is tough, but your awareness alone puts you ahead of most people.
This post cuts to the heart of a common misconception: prompt engineering isn't a solution to bad architecture. While it can provide quick fixes, it only highlights deeper structural issues in your system-like inconsistent APIs, missing domain boundaries, and scattered business logic. Relying on AI to mask these problems creates fragile systems that fail under real-world conditions.
The key takeaway: AI amplifies your architecture, good or bad, and prompt engineering can only expose, not solve, these foundational issues. Address your architecture first, then let AI do its job.
This is very true. People often blame the prompt, but rearranging garbage still gives you garbage. That part is easy to forget.
You’re absolutely right—this is a sharp and thoughtful observation. I really like how clearly you cut to the core of the problem without overcomplicating it.
that's why capturing intent is important, and tools like:- TraycerAI, Kiro, Spec-kit can be used
Absolutely — that’s a great point. Capturing intent early makes everything clearer downstream, and tools like TraycerAI, Kiro, and Spec-kit are smart choices to turn that intent into clean, actionable outcomes.
Pretty good.
Best wishes
Best wishes.
I totally agree with you!😃
Thanks for your response.
Good point of view, keep it up.
Thanks.
Best wishes.
It really fixes. have you used claude max subscription
Yes — I’ve used Claude Max, and while the larger context window helps with long-form reasoning, it doesn’t fix architectural or modeling issues-it mainly reduces friction for complex workflows rather than changing core capabilities.
Have you seen the wit project on GitHub
Just go to GitHub and search for wit. You would see the repo just take a few minutes and explore it.
There is always something new to learn and evolve
Yeah, thanks.
You are right.
We have to focus on improving our capabilities without prompt.
Love this! Its why I just published dev.to/jbobbink/17-common-seo-mist... Some self help content for LLMs.
Love that approach—sharing practical SEO insights for LLMs is both timely and generous. Great work putting this out there; it’s exactly the kind of content that helps the whole community level up 🚀
Woow.
That's great.
I am new here, but I have reviewed your post carefully.👏
thanks.
Hello!
That sounds good.
❤
Thanks.
Interesting article
Thanks😎
well.
👋👋👋👋👋
thanks.
Good breakdown👍
thanks.
you are right.
I also think it's not a true way to build sth amazing with a prompt.
thanks.
Totally agreed. The prompt engineering to help MVP fast not for production ready. Thank you for sharing
Good.
That's right.
Yeah.
Dear Art.
that's great.
thanks.
"Prompt Engineering Won’t Fix Your Architecture" Louder for the people in the back!!
Absolutely! 👏 Spot on — no amount of clever prompts can patch a shaky foundation. Solid architecture is where real magic (and reliability) starts!
Prompt engineering is useful, but only after you’ve fixed the foundation. When a team spends all its time tweaking prompts, it usually means the underlying system is messy the AI is just reflecting the confusion in the architecture.
That's good.
This really resonated with me. The line about “AI doesn’t replace architecture, it amplifies it” sums it up perfectly.
I’ve noticed the same pattern — prompts often become a way to hide deeper problems instead of fixing them. When data models are inconsistent or business logic is scattered, no amount of “be careful” in a prompt can save the system.
The distributed systems comparison is especially accurate. Once AI is in the loop, all the usual issues show up again — state, retries, side effects — just wrapped in natural language.
Good architecture makes AI feel boring and predictable, which is actually what you want in production. Bad architecture makes demos look magical and production feel fragile.
This post is a good reminder that fundamentals still matter, even in an AI-heavy world. Appreciate you putting this into words so clearly.
This resonates. I've been experiencing the same thing from the other side - what happens when you do invest in fundamentals first.
When your architecture is consistent (IaC, clean service layers, good test coverage), AI becomes boring and predictable in the best way. Claude's suggestions usually follow existing patterns instead of inventing new ones. Refactoring is safe because tests catch regressions.
"AI amplifies architecture" is exactly right. I wrote about my experience building a data pipeline platform solo with this approach.
I like how you show that investing in strong fundamentals actually makes AI more reliable instead of chaotic — that’s exactly the outcome I’d hope for. The idea that AI becomes “boring in a good way” when architecture is solid feels like the right long-term solution.
Great.
Thanks for your response.
If you need help, please.
Unzip and deploy, fix architecture in minutes 👍
This was very good. I had been thinking of a good way to put this to words. Nicely done!
Appreciate that—glad it resonated. I was aiming to articulate something many of us intuitively feel but rarely state clearly.