Brandi Kitchens
The Beginning Looked Simple
When I started building RiskLens CI, the idea actually made sense to me.
- Trigger on a GitLab merge request
- Analyze code changes
- Return risk level, issues, and recommendations
Conceptually… I understood it.
But the system itself? Completely different story.
It was running. The endpoint responded.
And I thought… okay this should be working.
It wasn’t.
This Felt More Advanced Than Anything I’ve Built
I’m going to be real here — this project felt advanced for me.
Not because I couldn’t understand the idea…
But because:
- The system behavior was unfamiliar
- The feedback loop wasn’t obvious
- And small mistakes didn’t break things — they just made them worse
That part messed with me.
The “Almost Working” Stage Is the Worst
Everything looked like it was firing correctly:
- Webhook → ✅
- Backend → ✅
- AI processing → ✅
But the results?
- Slightly off
- Missing detail
- Not as “smart” as I expected
That’s when frustration really started setting in.
Because nothing was clearly broken.
Risk & Predictive Systems Are Not Forgiving
This is something I didn’t expect:
Risk assessment systems and predictive outputs are extremely sensitive.
If you’re not precise:
- The risk score becomes meaningless
- The issues feel generic
- The recommendations lose value
And that’s exactly what I was seeing.
It wasn’t wrong… it just wasn’t useful.
What I Was Missing (And Didn’t Realize)
This is what took me the longest to understand:
I wasn’t missing logic — I was missing language
Sometimes it was:
- One phrase in the prompt
- One missing instruction
- One unclear expectation
And that tiny gap changed everything.
When It Finally Clicked
This is when it started to feel real:
Now I was getting:
- 🔺 Risk Level: HIGH (75/100)
- Clear summary of what was happening
- Real issues tied to configuration + authentication
- Recommendations that actually made sense
This wasn’t just output anymore.
This felt like a system.
What Changed Everything
I stopped thinking:
“Why isn’t this working?”
And started thinking:
“What EXACTLY am I not telling the system?”
That shift helped me:
- Tighten my prompts
- Be explicit about output format
- Remove assumptions
- Treat the AI like a strict executor
And that’s when everything came together.
The Frustration Was Real
There were moments where I felt like:
- I was going in circles
- The system was reacting but not improving
- I was right there… but couldn’t see what was missing
That “almost working” phase is exhausting.
But it’s also where everything starts to click.
What I Learned
Precision > Complexity
Even simple ideas break without clear instructions.
AI Doesn’t Guess
If you don’t say it, it won’t do it.
Predictive Systems Require Control
You can’t be vague with risk analysis — you have to define everything.
Final Thoughts
If you’re building AI systems and feel stuck…
You’re probably not far off.
You might just be missing:
- one phrase
- one instruction
- one piece of clarity
And once you find it — everything changes.
What’s Next
I’m continuing to evolve RiskLens CI into:
- AI-powered code review assistant
- Multi-agent analysis workflows
- Automated fix + deployment previews
This is just the beginning.
If you’re building something and it feels frustrating — especially with AI — you’re not alone. You’re probably closer than you think.