Using prompt engineering, multi-model comparison, and context-aware chaining with Crompt AI
Most developers rely on Git like muscle memory.
Until something breaks.
Merge conflicts. Detached heads. Rebase loops.
Suddenly, you’re copy-pasting errors into Stack Overflow at 1 a.m., hoping someone had the same obscure setup—and remembered to mark it “solved.”
But what if you didn’t just query Git issues?
What if you had a custom-trained AI assistant that understood your specific repo structure, branching strategy, and local habits—
and could debug your workflow like a teammate?
That’s what I built.
Not a full-blown custom-trained model (no finetuning needed).
Just a clever chaining system using multiple AI models, prompt patterns, and memory—all stitched together through Crompt: an all-in-one AI chat platform for developers who want to build smarter workflows, not just chat with GPT.
Here’s how I did it.
The Problem: Git Advice Is Too General
I didn’t want “generic” help.
I wanted:
Help that could understand my branching model
Reason through error logs in context
Suggest fixes based on what I already tried
Offer different perspectives if one model failed
What I didn’t want:
Reddit threads from 2016
Unhelpful ChatGPT-3.5 advice like “Try git reset --hard”
Repeating the same prompt across 3 different windows
Step 1: Creating the Debug Prompt Template
The first step was to define how to describe my Git problem clearly to any model.
I settled on this format:
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[CONTEXT]
- Project type: (e.g., monorepo, microservices, etc.)
- Git strategy: (e.g., Git Flow, trunk-based)
- Recent action: (e.g., rebase, cherry-pick)
- Observed issue: (error logs, behavior)
- What I tried so far: (commands, results)
[GOAL]
What should I do next, and why?
Instead of rewriting this every time, I built a basic shell function that collected:
git status
last 10 commits (git log -n 10)
git branch -vv
error logs (via stderr redirect)
Then I dropped all of that into my prompt payload.
Step 2: Running Multi-Model Comparison with Crompt
Most developers default to GPT-4 or Claude. But model plurality is key here.
Using Crompt’s multi-model interface, I tested the same prompt against:
GPT-4o: often structured, but overly cautious
Claude 3.5 Sonnet: excellent for reasoning through branching strategies
Gemini 2.0 Flash: good for command suggestions, less so for edge cases
Mistral AI: surprisingly good at matching git CLI nuance
What I noticed:
Claude gave the most human-like analysis
GPT-4o offered reliable but sometimes verbose steps
Gemini excelled at commands but lacked full context
Grok 3 Mini gave blunt but useful "just do this" answers
I used Crompt’s side-by-side model view to compare responses live and choose which “thinking style” to follow.
Step 3: Layering Memory Into the Conversation
Git problems are rarely solved in one shot.
So I needed context persistence.
With Crompt’s interface, I could upload my session logs or even full diffs as documents, then use Document Summarizer to give the model a quick memory of what’s been done.
Each new question I asked built on the last.
I even created a prompt chain like this:
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First: Summarize my current Git issue and what I’ve tried.
Then: Predict what’s likely causing the issue based on the strategy.
Finally: Suggest two different ways to solve it with explanation.
Instead of stateless chat, I got stepwise reasoning.
Step 4: Embedding Into My Workflow (Minimal Setup)
I didn’t want to leave the terminal.
So I created a tiny CLI wrapper that:
Sent my prompt template + collected Git logs to Crompt’s chat endpoint
Let me choose which model to query
Printed a summary of top 2 suggestions directly in the terminal
Bonus:
If I wanted visual context (like which branch is tracking what), I could paste it into Crompt’s AI Chart Generator and get a diagram.
Unexpected Wins
The AI learned my repo patterns over time. The more logs and commits I fed in, the better it contextualized suggestions.
No more wild command guessing. Most responses came with warnings, side effects, and rollback options.
I built a reusable system. Now any teammate can plug in their issue and get model-backed suggestions in minutes.
Final Reflective
Most developers treat AI as a chatbot.
But if you treat it like a debugging assistant, you unlock a deeper layer:
Multiple models = multiple points of view
Prompt chaining = layered logic
Context memory = cumulative problem-solving
You don’t need to fine-tune a model or write a plugin.
You just need the right system.
And tools like Crompt—where all top AI models, from GPT-4o to Claude 3.5 Sonnet to Mistral and Gemini, live in one place—make that possible.
It’s not just about fixing Git.
It’s about thinking more clearly in complexity.
And as any dev knows, that’s the real superpower.
-Leena:)
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