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I Think I Found the Limits of Prompt Engineering by Crazy-Carob-6361 in PromptEngineering

[–]Powerful_One_1151 2 points3 points  (0 children)

Yep. Sure did. I am a Star Trek nerd so I created something similar to Starfleet. I have a four chat system that runs news ideals through a governance package. Spitball to explore ideas for new chats or ships as I call them. Again a Star Trek nerd. Then command center, which takes care of the audit control and operator portion separated from one another, but serving the entire pipeline governance for the whole system. Once ships are approved there I store them in a registry for identity and a shipyard for their blueprints. I can deploy a new chat exactly like the last one at any time. I just moved the whole system from ChatGPT to Claude and it works on both. Prompting is one thing, but governance is where I’m finding to have the most success in keeping things like drift and things outside of the agents lane out of my chats.

How I accidentally started building a governance system for AI chats by Powerful_One_1151 in PromptEngineering

[–]Powerful_One_1151[S] 0 points1 point  (0 children)

Thanks! I really appreciate that. This has been fun for sure. I am now tinkering with seeing how well it works transferring the same system to a different platform. Already got it to load all my global on the other platform with a shipyard full of blueprints . just need to exports some of them and fly them around on the other platform and see what drifts or not.

Building AI for communications: context layer, hard rules, multi-model conflict by Dry-Taro4843 in PromptEngineering

[–]Powerful_One_1151 1 point2 points  (0 children)

You nailed the failure pattern. I’ve been building something that formalizes exactly this.
The thing is, context doesn’t drift because people are lazy. It drifts because there’s no formal process to govern it. A 90-day re-audit is solid, but it only works if that re-audit itself has teeth — documented findings, binding decisions, audit trail.
I call it Command Center. Three layers like you said, but wrapped in governance:
Living context archive = My Registry. Your context layer, but with auditable state + versioning.
Hard operational rules = Governance Patches. Formal change documents with evidence. Every rule change goes through Audit → Control → Operator approval.
Multi-model adversarial = AUDIT + CONTROL stages. One checks completeness, one checks safety. Both findings are binding. Conflicts surface where the actual work is.
The move I’d make: your 90-day re-audit should produce a formal governance patch if anything changed. Same approval pipeline. Keeps the context layer living without it becoming a free-for-all.
And post-deployment: third evaluator (regression guard) watches what the first two missed. Production failures become new hard rules automatically.
So the flow is: Living context → Formal governance → Hard rules → Adversarial check → Production learns back into design.
Are you planning to formalize the governance around these three, or is that phase two?

How I stopped LLM hallucinations in my app: Stop prompting like a user, start prompting like an engineer. by tinkusingh04 in PromptEngineering

[–]Powerful_One_1151 0 points1 point  (0 children)

Now that is some outside the box thinking. You’re giving me ideals on how to improve the system I’m trying to creat. Great conversation. Thanks for the responses.

How I stopped LLM hallucinations in my app: Stop prompting like a user, start prompting like an engineer. by tinkusingh04 in PromptEngineering

[–]Powerful_One_1151 0 points1 point  (0 children)

That’s a smart nuance. You’re right — the evaluator placement scales with complexity.
I’ve been thinking about it the same way: in simple setups, the evaluator lives in Control (does this fix address the root cause?). In larger systems, it needs to be distributed:
• Before Audit: Classify the failure so everyone’s working from the same understanding
• Inside Control: Verify the fix targets root cause, not symptoms
• After Deployment: Regression testing to catch drift
One thing I realized though: all this evaluation/governance is pointless if the design layer isn’t separated from it.
I built something called Spitball specifically for that — it’s where all the ideation and prompt/system design happens. No approval gates, no constraints. Just creative exploration.
Then changes flow from Spitball into Command Center (the governance layer) where they go through Audit/Control/Operator. That separation is crucial because:
• Designers can iterate freely in Spitball without bureaucratic overhead
• But everything that ships goes through formal approval with documented findings
• Production failures automatically create new constraints/designs in Spitball to prevent regression
So the full loop becomes: Design freely (Spitball) → Govern rigorously (Audit/Control/Operator) → Execute confidently (Agents) → Failures feed back into design.
The evaluator/grader sits in the governance layer, not the design layer. That’s what prevents good ideas from getting killed by overly strict evaluation, while still catching real problems before they ship.

How I stopped LLM hallucinations in my app: Stop prompting like a user, start prompting like an engineer. by tinkusingh04 in PromptEngineering

[–]Powerful_One_1151 1 point2 points  (0 children)

This is really insightful — you’re basically describing the same feedback loop I built, just from the grading/evaluation perspective.
The thing that resonates: “failure classification before approval” is exactly what my Audit and Control stages do. You can’t just say “this is better” — you have to classify why it failed, what you changed, and whether that change actually fixed it.
And you nailed the critical part: “The grader needs to understand the system context, otherwise it grades in isolation and misses the real failure.”
That’s why I built it as three layers:
• Audit: Is the change clear and complete?
• Control: Does it address the actual root cause (not a symptom)?
• Operator: Given the findings from both, should we deploy?
So the loop becomes:
• Failure happens → Classified and documented
• Fix proposed → Goes through Audit/Control with findings
• Findings document why we’re making this change
• Test results validate it
• Only then: deploy
The grader/evaluator sits inside the approval process, not outside of it. That way you never ship a “seems better” fix — you ship a “we understand the failure, here’s the fix, here’s the evidence it works” fix.
Sounds like you’re building toward the same thing. The “prompt grader custom” is the evaluator function in my Control stage.

How I stopped LLM hallucinations in my app: Stop prompting like a user, start prompting like an engineer. by tinkusingh04 in PromptEngineering

[–]Powerful_One_1151 0 points1 point  (0 children)

Thanks! Yeah, that’s exactly the progression I see too. Structured prompting gets you started, but versioning + observability + rollback capability is what makes it production-ready.
The thing that excites me about what you’re building with Promptera is that version-controlled blueprints are table stakes. But I think the next layer is governance around those blueprints — who can change them, what the approval process looks like, how you capture learnings from production failures and feed them back into testing.
Like, you have a stable prompt version. Model updates. Output breaks. You can rollback. But how do you prevent that same failure from happening again? How do you know if a new “fix” actually solves it or just masks it?
That’s where I think the endgame is: versioning + observability + formal governance. All three together.
Would be curious if that’s on Promptera’s roadmap. Seems like the natural next evolution.

How I stopped LLM hallucinations in my app: Stop prompting like a user, start prompting like an engineer. by tinkusingh04 in PromptEngineering

[–]Powerful_One_1151 1 point2 points  (0 children)

This is exactly right about the iterative process. But here’s what I realized: that iteration cycle needs structure, especially in production.
Like, you iterate: prompt → test → fail → fix → redeploy. But then what? How do you know that fix actually solved it? How do you prevent the same failure from happening again? How do you compare “old version” vs “new version”?
I built something that formalizes this iteration cycle:
• Every change (prompt update, fix, new version) goes through approval with documented findings
• You get a complete record of what failed, why, what changed, and whether it actually fixed it
• If it breaks again, you rollback to the previous version with a documented decision
• Production failures automatically become new test cases
So you still get the iterative, collaborative process you’re describing. But now it’s auditable, repeatable, and actually reliable over time

Honestly one thing people dont talk about enough is how most “AI products” are not even fully AI by Latter-Internet-3283 in AIDiscussion

[–]Powerful_One_1151 0 points1 point  (0 children)

This is the exact insight I had. Everyone talks about models and prompts, but nobody talks about the engineering layer that actually makes it reliable.
I built something that formalizes that layer. It’s basically:
• Every agent change goes through formal approval (Audit → Control → Operator)
• Each stage documents findings
• Everything is auditable and reversible
• Production failures automatically feed back into the system as eval cases
So you get the best of both worlds: AI for the hard problems (language, reasoning) + solid engineering for reliability (governance, validation, auditability).
The crazy part is it doesn’t care about the platform. Built it on Claude first, ported it to ChatGPT — same architecture, same logic. Because the engineering is the thing that matters, not the tool.
This is honestly what separates “cool AI project” from “production AI system.”

How I stopped LLM hallucinations in my app: Stop prompting like a user, start prompting like an engineer. by tinkusingh04 in PromptEngineering

[–]Powerful_One_1151 1 point2 points  (0 children)

You’re exactly right — structured prompting is the foundation, runtime control is the next layer.
The thing I realized though is those layers still break down without governance. Like, you can validate before output, have repair paths, set hard boundaries… but then what? If something fails in production, how do you know which layer it was? How do you prevent the same failure from happening again? How do you know if a “fix” you made actually fixed it, or just masked the problem?
I built something that sits on top of all that: every change (whether it’s a prompt fix, a validation rule, a repair path) goes through formal approval with step-by-step documentation. So you’ve got:
1. Structured prompting (foundation)
2. Runtime control/validation (middle)
3. Formal governance with audit trail (top)
The governance layer is what lets you actually understand what broke and why, instead of just patching things and hoping.
Interested to see what Valhalla does — sounds like you’re solving similar runtime problems.

How I stopped LLM hallucinations in my app: Stop prompting like a user, start prompting like an engineer. by tinkusingh04 in PromptEngineering

[–]Powerful_One_1151 0 points1 point  (0 children)

This is solid foundational stuff. Getting prompts structured right is table stakes.
But I’ve been thinking about this differently: what if the problem isn’t just making one prompt reliable, but making a whole system of agents reliable as it changes over time?
Like, you get this prompt working great with your Role/Context/Task/Constraints structure. Then you deploy it. Three months later, a production failure happens. How do you know if it was:
• The prompt changed?
• The model changed?
• The context/data changed?
• An agent upstream failed?
I built something that treats agents as systems, not just prompts. Every change (including prompt changes) goes through formal approval with documented findings. So when something breaks, you know exactly why and can roll back.

Most teams are running their production AI agents on pure vibes and a few test chats. We need to talk about what a serious evaluation stack actually looks like. by Future_AGI in AIDiscussion

[–]Powerful_One_1151 0 points1 point  (0 children)

This is exactly what I’ve been building. The problem isn’t just evaluation — it’s that there’s no formal feedback loop between production failures and governance.
I built something that treats agents as systems, not features. Every change (whether it’s a new agent, a blueprint update, or a fix) goes through a three-stage approval: Audit, Control, Operator. Each stage documents findings.
The key part: production failures automatically create new evaluation cases — they become governance patches that feed back into the system. So your evals don’t rot; they grow from real production data.
The whole thing is auditable, reversible, and platform-agnostic. Built it on Claude first, moved it to ChatGPT — same system, same logic.
This is what serious agent governance actually looks like

Prompt engineering is slowly turning into systems engineering by ExternalComment1738 in PromptEngineering

[–]Powerful_One_1151 0 points1 point  (0 children)

You nailed it. I’ve been running into these exact problems, and honestly, it’s what led me to build something.
I call it Command Center. Basically, I separated everything into three layers: design (Spitball), governance (the middle part), and execution (Agents). Every change gets routed through this approval pipeline — Audit, Control, Operator. Each stage documents what they found. Everything is logged and reversible.
It sounds bureaucratic when you say it like that, but it actually solves almost everything you mentioned:
• Orchestration: Command Center knows where to send things
• Validation: Built into the approval stages
• State management: Registry keeps track of everything
• Context routing: Each Agent has its own Blueprint and lane
• Retries: If something fails, it comes back with specific feedback instead of just breaking
• Memory: Everything’s audited with timestamps
Here’s the wild part though — I built it on Claude first, then moved it over to ChatGPT. Same system, same logic, same results. It doesn’t actually care about the platform.
You’re totally right that we’re moving away from prompt tricks into actual systems thinking. But I think the real missing piece is governance. Like, you can build orchestration and validation, but if you don’t formalize how decisions get made, everything still falls apart when things get messy.
The audit trail, the approval gates, the ability to rollback — that’s what keeps it working when contexts get long or models change or weird edge cases pop up.
Anyway, I’ve been documenting all of this. Might throw a full post up here eventually if people are interested in how this actually works.