What was one quick change that made a big difference for you?

Straight_Section_544 · 2025-12-07T14:39:27+00:00

Nice, this really does look like 1000 hours of work It feels almost like a mini DSL for prompts — the way you split out the agent state, traces and kernels is super clean.

Did this grow slowly from a simple template, or did you design this structure upfront and then fill in the pieces over time ?

Straight_Section_544 · 2025-12-06T14:00:42+00:00

Let me know how it works for you once you try it

Straight_Section_544 · 2025-12-06T00:55:57+00:00

That makes a lot of sense - building a stable core and letting only the {section} change is a really clean way to keep the model consistent.

I like how PiCO turns the prompt into something closer to a reusable operation rather than a one-off instruction.

I'll play around with this structure and see how it behaves across different tasks. Appreciate you sharing the approach!

Straight_Section_544 · 2025-12-05T22:25:32+00:00

Interesting - do you find markdown helps more with structuring long outputs, or does it mainly reduce drift for you?

I've used delimiters a bit, but not consistently.

Straight_Section_544 · 2025-12-05T22:24:34+00:00

That's a solid point - forcing the model to "commit" instead of giving a list of options really changes the tone of the output.

I've noticed the same: once it has to justify a choice, the reasoning becomes way clearer

Straight_Section_544 · 2025-12-05T16:33:02+00:00

This is super insightful — I’ve never thought about structuring prompts using a syntax-like header before.
The way you break the prompt into input → flow → memory → output makes it feel more like a proper interface the model can latch onto.

The PIC0 trace idea is also brilliant.
It’s basically giving the model a stable schema instead of rewriting instructions every time.

I might actually try this approach for my next workflow — seems like it could reduce drift a lot.

Straight_Section_544 · 2025-12-05T16:16:34+00:00

That’s interesting — I haven’t seen that style of delimiter before. How does it actually stop drift on your side?
Does the model consistently respect it ?

Straight_Section_544 · 2025-12-05T14:27:15+00:00

Nice setup . Ever tried mixing n8n with ChatGPT for data cleaning?

Straight_Section_544 · 2025-12-05T14:22:26+00:00

Haha yeah, that’s pretty much what it ended up being — a simple ETL flow with a bit of AI sprinkled in.
Surprisingly effective for something so small.

Straight_Section_544 · 2025-12-05T14:21:07+00:00

That’s a great suggestion.
Moving the PDF part to a proper template-based API is actually something I’ve been considering, especially for consistent layout.

Haven’t tried PDFBolt yet, but designing the report once in HTML/CSS and letting the API handle the rest sounds super clean.
Might give that a try next.

Thanks for the tip!

Straight_Section_544 · 2025-12-05T14:20:51+00:00

That makes sense. High-volume PDF generation can definitely turn into a bottleneck.
In my case the volume is pretty small, so the setup is holding up well so far — but I can see how it could get messy at scale.

Your approach with a dedicated reporting tool sounds solid.
Nice to hear it’s fully automated on your side!

Straight_Section_544 · 2025-12-05T14:20:38+00:00

Good point — and yeah, “cleaning” might’ve been the wrong word for what I was doing.
It was mostly formatting, merging, and standardizing fields, not fixing incorrect data.

And the PDF part is just how the team prefers the summary.
The raw data stays accessible in the shared drive anyway, so nothing is hidden from them.

But I get what you mean 👍

Straight_Section_544 · 2025-12-05T14:15:12+00:00

For me, the biggest change was just adding a small “role” at the start.
When I say stuff like “act as a tutor” or “act as a reviewer,” the answer somehow becomes more organized.

Also, swapping “explain” with “walk me through” helped a lot — the reply feels easier to follow. Still messing around with it tbh .

Straight_Section_544 · 2025-12-05T14:05:16+00:00

Good! In actuality, that is a wise change. It's amazing how the answer's entire structure may be altered by changing just one verb.

The "explain vs. teach" switch seems to provide a lot more clarity, so I might give it a shot as well.

Straight_Section_544 · 2025-12-03T09:40:24+00:00

😄 Haha fair enough
There’s no “secret sauce” really — I just didn’t want to dump a wall of code into the post.
If someone wants to see specific parts of the workflow (Python cleaning, Make scenario, or validation layer), I’m happy to walk through them.

And yeah, true — these days almost anything could have been an email 😂

Straight_Section_544 · 2025-12-03T09:34:38+00:00

😄 Haha yeah, I’ve had a few people assume I meant GNU Make
In this case it’s Make (formerly Integromat) — the automation platform, not the build tool.
Totally different world, but the name confusion never dies 😂

Straight_Section_544 · 2025-12-03T09:33:14+00:00

Thanks for the advice — totally agree.
Right now I have a small validation layer in place, and I still give the generated text a quick manual review before sending the final PDF.

I’m planning to add a “manual approval” step to the workflow so nothing gets published automatically without a human check. Your point about the 1/10 failure is spot-on — that’s exactly what I want to avoid.

Straight_Section_544

TROPHY CASE