u/Ok_Dog3219

I’ve been building small automation workflows recently, and one pattern keeps repeating.
People try to:
replace everything with AI
Instead of:
simplifying the workflow first

What I’ve seen work better:
remove unnecessary steps
define inputs/outputs clearly
THEN add AI where it makes sense

Example:
Instead of:
AI agent doing everything
Better:
step 1: collect data
step 2: AI extracts info
step 3: system validates + stores

Result:
more stable
easier to debug
actually usable

AI is powerful, but without structure it just automates chaos.

How are you guys using AI in workflows — fully automated or hybrid?

I kept doing repetitive data tasks manually, so I built a simple automation.
It:
collects data
processes it
outputs structured results
Nothing fancy, but saves a lot of time.
Now I’m curious:
what repetitive tasks are you still doing manually?

I’ve been building RAG systems and one thing I see all the time:

People focus on the model, but ignore retrieval.

Common issues:
bad chunking
no metadata
irrelevant context

What actually matters:
retrieval quality
structure
filtering

Improving this changes results way more than switching models.
Curious what approaches others are using.