Or: what this book actually teaches if you read it like an engineer, not a magician.
Prompt-Engineering_v7-1.pdf
After my last post(Prompt Enginee...
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Great read!
I tried to fix this in my own projects by applying Separation of Concerns and Type Safety. I ended up building a boilerplate called Atomic Inference (Jinja2 + Instructor + LiteLLM) to handle exactly this. It separates the messy prompt logic from Python code and ensures the output is always structured.
It basically turns the guessing game into actual engineering. Curious to hear your thoughts my repo: github.com/chnghia/atomic-inferenc...
Great approach—this really feels like a practical step toward treating LLM work as real engineering instead of trial-and-error. I like how Separation of Concerns and strong typing turn prompts into something predictable and maintainable, which is exactly what most projects are missing right now. I’m definitely interested in digging into Atomic Inference and seeing how this pattern could scale in more complex systems.
Thanks! Scale was actually the main reason I built this.
I’m currently running it in a Personal Agentic Hub with 10+ agents. The 'Atomic' approach makes composing them into larger workflows (in LangGraph) much less painful than managing a massive prompt inside codebase.
Love this approach — designing for scale from the start really shows here. Running 10+ agents cleanly is no small feat, and the Atomic model feels like a genuinely practical way to keep complex workflows sane and maintainable.
The SQL-on-bad-schema analogy is clever, but incomplete. Bad schemas are accidental; probabilistic models are intrinsic. Prompt engineering isn’t compensating for a flaw—it’s adapting to a fundamentally different computation model. That’s closer to writing concurrent code or numerical methods than patching a mistake. Entire disciplines exist to manage nondeterminism.
That’s a fair distinction: nondeterminism isn’t a defect here, it’s a core property of the model. Prompt engineering is less about correction and more about applying established techniques for controlling stochastic systems—much like concurrency, optimization, or numerical stability.
I mostly agree with your framing, but I think you’re underselling something important: prompt engineering is architecture—just at the human↔model boundary. Yes, it exposes ambiguity, but designing constraints, interfaces, and failure modes at that boundary is real engineering work. We don’t dismiss API design because it’s “just middleware.” The fact that it looks like text doesn’t make it less structural.
Great point—prompt engineering is architectural work at the human–model boundary, shaping constraints, interfaces, and failure modes much like API design does. Treating it as “just text” misses how much system behavior and reliability are determined by that layer.
This hits hard. “Prompt engineering is middleware for probabilistic systems” is probably the cleanest framing I’ve seen. The part about prompts exposing weak architecture is painfully accurate — the model isn’t confused, the system is. Great read.
Thank you — I really appreciate that. I’m glad the framing resonated, and it’s encouraging to hear it landed clearly with someone who sees the architectural implications so sharply.
Yeah this is the first “prompt engineering” take I’ve read that actually lives in reality.
The SQL/bad schema analogy is brutal because it’s true: you can get results, but you’re basically building a career around compensating for upstream mess.
What clicked for me is your framing of prompts as control systems. That’s exactly it. Chain-of-thought, ReAct, schemas, “be consistent,” temperature… it’s not wizardry, it’s you trying to pin a probabilistic blob into something contract-shaped so the rest of your system doesn’t explode.
And the deeper point is even nastier (in a good way): LLMs don’t just fail weirdly — they expose how vague our requirements are. The model isn’t “smart” when it makes a call. It’s just forcing you to admit you never defined the boundary in the first place.
So yeah, prompt engineering is real… the same way duct tape is real. Useful, sometimes necessary, but if your whole product is duct tape, you don’t have a product — you have a future incident report waiting to happen.
This post is basically: stop worshipping prompts, start owning constraints. That’s the actual skill ceiling.
This nails it — treating prompts as control surfaces rather than magic spells is the only framing that scales in real systems. The uncomfortable truth you call out is the key insight: LLM failures aren’t anomalies, they’re diagnostics for undefined constraints, and mature teams learn to fix the system, not keep adding duct tape.
👌👌👌BRILLIAT!!!👍👍👍👍
This resonated more than most takes on prompt engineering. What clicked for me is the framing of prompts as control surfaces rather than “clever instructions.” Every technique people label as “advanced prompting” looks exactly like what we already do when integrating unreliable systems: constrain inputs, force schemas, narrow responsibility, and assume retries will happen.
The uncomfortable part (and the useful one) is that LLMs don’t let you hand-wave ambiguity anymore. If you say “pick the reasonable option,” the model just reflects your unresolved business logic back at you — confidently. That’s not intelligence, it’s a mirror.
Wish your regard...
From dev
I also appreciate the distinction you draw between advisory use and authoritative use. Prompt engineering works when failure is cheap and reversible; it becomes technical debt the moment we let prose substitute for domain rules. At that point we haven’t built AI — we’ve built a distributed system with no contracts and very persuasive error messages.
Framing this as “middleware for probabilistic systems” is probably the most honest description I’ve seen. Not a career path, not a spellbook — just another layer that forces engineers to finally design the boundaries they were getting away without before.
This is a really sharp take — I love how you ground prompt engineering in real engineering instincts instead of treating it like magic. The “control surfaces” framing especially matches how I think about designing for failure and retries in messy systems, and it sets a much healthier expectation for how LLMs should be used. I’m genuinely interested to see how this mindset shapes more concrete patterns or tooling, because this feels like the right foundation for building things that actually last.
This is the best framing I've seen. I've been on both sides of this.
When I started using Claude Code, my prompts were long and defensive - "don't do X, remember Y, watch out for Z." Classic symptom. The AI was exposing that my codebase didn't encode its own rules.
Now I put those boundaries in CLAUDE.md files: "handlers delegate to services," "use constants not strings," "this is the error handling pattern." The prompts got shorter. The AI got more consistent.
The prompt engineering didn't get better - the system did. The AI just stopped needing to be told what the codebase should have already said.
This resonates deeply—LLMs act like boundary detectors, surfacing implicit architectural debt the moment rules aren’t encoded in the system itself. Once invariants live in code and docs instead of prompts, the model simply amplifies good design rather than compensating for its absence.
Nice perspective on prompt engineering as middleware. The comparison to distributed systems problems makes sense for handling LLM quirks.
Thanks! That analogy really helps ground the discussion in familiar engineering tradeoffs, and I’m glad it resonated with how you think about system reliability and constraints.
Great article 👏👏
Thanks 😎
Great!
Thanks😎
That sounds good👍👍👍👍👍👍
Thanks😎
I have just started to explore ai coding tools again. I was stubborn and didn't want anything type my code other than auto complete from LSP.
I'm kind of excited. Personally, what I'm feeling is
it's a great tool I can write code really fast I couldn't imagne but within my less than 100% ability. Because I don't think and I just give instructions based on what I know.
All the chains coming to me while I'm working on it, slowly, and think.
But the productivity ai tools do is something my brain can't catch up with.
Having said that, I'm excited to expand my capability more, than I can do more stuff with ai tools.
And also, it's not like a traditional way to code. I'm working as a web developer. There are data scientists, ai developers, devops, and you name it.
This engineering is a different job. It's related to software development, it does write code, but it's not what I'm doing. It's a different category.
Lastly, it feels like a social media. It can make you lazy, very much of it, unless you're in a place you have to concentrate or you have a strong mentality. Smoke, drink, shorts and whatever it does nothing to you, because you have a strong mentality then it's fine.
However, as a regular person, being myself, it's so addictive to skip what I should do. I really need to be careful with it.
What I've written is quite out of the topic on this post just random thought, wanted to leave a comment.
Thanks for the post 👍 I will look up the book
This hit the nail on the head — prompt engineering isn’t magic, it’s like middleware for probabilistic systems. It just forces you to define clear boundaries and structure in your architecture instead of hiding ambiguity. Great reminder that prompts expose problems rather than fix them.
This reads less like an anti–prompt-engineering rant and more like a systems reality check. Framing prompt engineering as middleware for probabilistic systems is spot-on it explains both why it feels powerful and why it breaks down without real architecture. The book’s real value isn’t the tricks, it’s how it forces teams to confront ambiguity, contracts, and boundaries they’ve been hand-waving for years
Calling someone who can ask questions from a machine that was specifically designed to answer questions an engineer is like calling me a heart surgeon because I successfully cut my fingernails.
This looks great!
this was cool, thanks for sharing!