I asked 3 AI engineers to try a workflow that automates the boring parts of prompt design, RAG pipeline setup, and LLM evaluation.
I expected feedback like: “the answers are smarter” or “the model feels better.”
That’s not what I got.
What they cared about was structure.
Not structure as a buzzword, structure as in:
“When I run this again tomorrow, will it behave the same way? Will it output something I can trust and reuse?”
Here’s what they told me, and what I learned from it.
What 3 AI engineers actually said
1) “Pre-built prompt standards matter more than clever prompting”
One engineer said they loved that prompt standards are already pre-built, and that output structure is treated as the main focus for quality.
That’s a big shift from how most of us start.
Most prompt work begins with:
- wording tweaks
- prompt length debates
- prompt “styles”
But engineering teams don’t win with prettier prompts.
They win with repeatable outputs.
When the structure is consistent (same fields, same format, same expectations), everything downstream becomes easier:
- planning becomes easier
- validation becomes easier
- debugging becomes easier
- agents become easier to orchestrate
In simple terms: structured output is a contract.
And contracts are what engineering workflows run on.
2) “It saves effort by removing the boring parts”
Another engineer said it saves a lot of effort because it removes the boring parts of:
- prompt design scaffolding
- baseline RAG pipeline setup
- LLM evaluation setup
This is the part people underestimate.
Not because it’s complex, but because it’s repetitive.
It’s the work that looks like:
_- “Let me rewrite the prompt template again.”
- “Let me set up ingestion/chunking/index again.”
- “Let me create an eval format again.”
- “Let me rerun this and compare outputs again.”_
It’s not hard work. It’s tax work.
And tax work kills momentum.
When you remove that tax, the engineer can spend time on the work that actually matters:
- what should we retrieve?
- what should we measure?
- what should the agent do when it fails?
- what’s the acceptance criteria for “good”?
3) “It matches how we already work, so it feels reliable”
The third note was my favorite.
They said the system knows exactly what steps we take for:
- prompt design
- RAG setup
- evaluation
…and that this creates a layer of confidence.
That confidence isn’t emotional. It’s practical.
Because most AI tools feel like you’re constantly doing translation work:
- translating engineering goals into “prompt language”
- translating messy outputs into structured decisions
- translating a pipeline into something repeatable
When the workflow already mirrors your steps, it feels like:
- you’re not fighting the tool
- you’re not re-explaining your process every time
- you can trust it to behave consistently
And that’s the foundation for scaling from “cool demo” to “real system.”
We are building HuTouch, a workflow tool that turns prompt standards, RAG setup, and eval setup into repeatable steps—so engineers don’t rebuild the same scaffolding every time.
The pattern I’m seeing: Structure beats cleverness
Across all 3 engineers, the theme was the same:
They didn’t want magic.
They wanted a workflow that:
- keeps outputs consistent
- removes repeated setup work
- matches real engineering steps
So here’s my current belief as a founder:
The real job isn’t making AI output impressive.
It’s making AI output dependable.
Because in production:
- inconsistency becomes bugs
- vague outputs become rework
- manual setup becomes burnout
If you’re building RAG or agents, try this (simple checklist)
If your pipeline feels “random,” don’t start by tweaking temperature.
Start here:
- Define a structured output contract (fields you always expect)
- Reuse prompt standards instead of rewriting prompts per task
- Treat RAG setup like infrastructure (repeatable, not artisanal)
- Make evals a first-class step (not an afterthought)
- Capture the workflow steps explicitly so re-runs feel identical
That’s it.
No fancy framework required.
Just structure.
If you want the visuals, here are the workflow mockups:
If you want early access to HuTouch, it’s here
And if you’re working on RAG/agents, tell me what drains you most right now: prompts, setup, or evals.
Top comments (0)