I spent 3 nights fighting AI hallucinations. Then I found this. 🕵️♂️🧩
Source: Dev.to
Background
I used to think building an LLM‑based app was simple: write a prompt, send an API request, get the result. I was wrong.
The Problem
In my latest project the model was brilliant one moment and completely hallucinated the next. My codebase turned into a spaghetti mess of concatenated strings, endless if‑else statements, and desperate logic checks. I had no idea where the chain was breaking:
- Was it my Python code?
- Was the context window too full?
- Or just a bad prompt?
I was about to scrap everything.
The Solution: Azure Prompt Flow
I stumbled upon a tool in the Azure ecosystem that hardly anyone talks about, but it changes the game entirely: Prompt Flow. It’s basically a debugger for the AI’s thought process.
Why it saved my project
- Visual graph – Instead of looking at walls of code, you see a visual graph where Python functions, LLM prompts, and API calls are linked like LEGO blocks. This makes it easy to spot exactly where the data gets corrupted.
- Parallel testing – Run different versions of a prompt against a dataset of questions in parallel.
- VS Code integration – A VS Code extension lets you run and debug these flows locally, so you don’t have to stay in the browser.
I stopped “guessing” and started engineering.
Who Should Use It
If you are building GenAI apps (RAG, chatbots, agents) and feel like you are losing control of your prompts, Prompt Flow can transform the “vibe‑based coding” into a structured workflow.
Getting Started
👇 Here is the official documentation that helped me start:
👉 Discover Azure Prompt Flow here
Call to Action
Are you using any specific tool to debug your LLM apps? Let me know in the comments! 👇