The first chapter in a 7-part series on mastering ChatGPT through 400+ real developer use cases. Based on 140+ real prompt workflows.
You’ve probably asked ChatGPT to write code for you.
You’ve debugged an error, rewritten a function, maybe even built half a project with its help. But if you’re anything like me (or the 140+ devs I studied), you’ve also wondered:
“Is this how I’m supposed to use it?”
“Why does this feel fast, but also… kinda empty?”
“Am I getting better — or just better at copy-pasting?”
This is Part 1 of The Dev’s AI Vault, a 7-part series built from over 400 real-world developer use cases including 140+ detailed prompt workflows.
In this first chapter, I’m cutting through the noise to answer a simple question:
How are developers actually using ChatGPT?
Not hypothetically. Not in theory. But day-to-day, in the real trenches of code, bugs, docs, meetings, and commits.
Let’s unpack them.
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Where the 400+ use cases came from (and what they taught me)
Before writing this series, I did what most devs do when they’re stuck: I opened 50+ tabs. But I wasn’t just skimming articles I was hunting for proof. I wanted to see how real developers were actually using ChatGPT. Not tutorials. Not demos. Workflows.
Here’s what I dug through:
1. OpenAI’s own Cookbook and Dev Guides
The OpenAI Cookbook isn’t just an API reference it’s packed with real examples: prompt chaining, code debugging, SQL generation, code review flows, even CLI wrappers. Between this and their official docs, I noted dozens of structured developer use cases.
2. GitHub threads, plugins, and integrations
I browsed through GitHub repos where developers had built VS Code plugins, terminal assistants, LangChain agents, and CI/CD automations and documented their prompt iterations and failures in Issues, PRs, and README files.
Examples:
3. Public blogs, walkthroughs, and dev logs
Many developers write “how I use ChatGPT” breakdowns from using it as a Python mentor to generating boilerplate code for geo-data pipelines.
Examples:
- Martin Breuss: Your personal Python tutor with GPT
- Tanya Tsui: LLM-powered data pipeline prompt walkthrough
4. Reddit, Discord, and community threads
I combed through dozens of long-form discussions on r/ChatGPT, LangChain Discord, and Hacker News comment threads where devs shared how they integrated ChatGPT into daily coding, debugging, or team flows.
These weren’t tutorials. These were troubleshooting threads, prompt rewrites, and devs explaining what worked and what failed.
5. Prompt databases and curated collections
From Aruva’s 100+ Dev Prompts to PyCoach’s Python debugging and Ahmed Besbes’s automation prompts, I gathered reusable patterns across categories:
- Bug-fixing
- Code review
- Comment generation
- Architecture and design summaries
- Regex creation
- Mentorship simulation
- SQL and data queries
6. My own ChatGPT workflow experiments
I also ran dozens of my own prompts, iterations, and integration tests from writing schema migration scripts to asking for architecture advice to using GPT for terminal helpers and cron job debugging. Many of those trials failed but I kept the working ones, optimized them, and added them to this vault.
Across all this, I ended up analyzing over 400 unique developer workflows, including 140+ real prompt examples from live use cases not prepped demos.
What I found wasn’t “prompt engineering.” It was something better:
Patterns.
Developers weren’t asking better questions; they were building better systems around the AI. And when you zoom out far enough, the same 9 usage patterns start to show up again and again.
That’s what this Part 1 of my 7-part series covers next.
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Pattern 1: Bug fixes and debugging without rubber-ducking
The most common use of ChatGPT isn’t flashy, it’s just debugging.
Instead of explaining a bug to a coworker (or your pet rubber duck), developers are pasting error messages, stack traces, or broken logic into ChatGPT. What’s surprising is how effective this is when done right.
How it works:
- Paste in the error and the problematic code block
- Ask for an explanation first, not just a fix
- Then request a corrected version or suggested approach
Real example:
“Here’s a React component. When I click the button, nothing happens. Can you help me debug it?”
Followed by: “Explain what thisuseEffectis doing and if it’s causing the issue.”
Why it works:
- ChatGPT is great at scanning syntax patterns
- It doesn’t get impatient like Stack Overflow trolls
- You get feedback fast enough to stay in flow
Dev Tip: If you just ask “what’s wrong?” without context, you’ll often get hallucinations. But framing the bug with clear intent leads to real insights.
Pattern 2: Generating boilerplate and scaffolding
This is where ChatGPT really flexes its productivity muscle. Need to scaffold a new Express route? Set up a TypeScript config? Write a basic Dockerfile? Devs are handing this to ChatGPT to cut the grunt work.
Common asks:
- “Generate a TypeORM entity for this schema.”
- “Write a basic Flask route that does X.”
- “Give me a starter Tailwind config with dark mode support.”
Real example:
A developer building a CRUD admin dashboard used ChatGPT to:
- Generate the initial Prisma schema
- Scaffold REST endpoints
- Generate unit test templates
- Write the README
The result? 2 days of setup collapsed into 45 minutes.
Why it works:
- Boilerplate is mostly syntax + structure
- ChatGPT handles repetitive patterns well
- It frees your brain to think about edge cases, not basics
Dev Tip: Ask ChatGPT to “comment every line” in generated code. This helps you learn while scaffolding.
Pattern 3: Refactoring legacy or messy code
Devs are increasingly using ChatGPT as a refactoring partner especially when working with untyped, legacy, or inherited codebases. You can paste in a large block of code and ask for readability, performance, or modularization improvements.
Common asks:
- “Refactor this into smaller functions with better names.”
- “Can you rewrite this to use async/await instead of callbacks?”
- “Make this more idiomatic in modern Go.”
Real example:
One developer took a 120-line Python script that parsed CSVs and asked:
“Break this into a reusable function, remove global variables, and explain any changes.”
ChatGPT returned clean modular code with explanations.
Why it works:
- You don’t always need correctness you need options
- ChatGPT surfaces alternatives that spark deeper thinking
- It forces you to see the code from a fresh perspective
Dev Tip: Don’t just accept the refactor. Ask: “Why did you break it this way?” or “What are 2 alternative approaches?”
Pattern 4: Writing tests (even when you’re too lazy to)
Testing isn’t glamorous, but it’s essential. Devs are using ChatGPT to turn existing code into unit tests, integration test stubs, and even mocking strategies, especially for unfamiliar frameworks.
Common asks:
- “Write unit tests for this function using Jest.”
- “Add test cases for edge conditions in this Python class.”
- “Generate integration test skeletons for these Express routes.”
Real example:
A developer pasted in a set of utility functions and asked:
“Write Jest tests for each one and explain what edge cases I might miss.”
ChatGPT not only wrote tests but also highlighted a missed null conditionsomething even senior devs forget under deadline pressure.
Why it works:
- Test writing is formulaic, but still time-consuming
- ChatGPT excels at coverage outlines and syntax scaffolding
- It nudges you to cover edge cases earlier
Dev Tip: Always ask: “What’s missing?” after generating tests. ChatGPT often won’t cover edge inputs unless prompted.
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Pattern 5: Architecture explanations and system summaries
Not every dev works in greenfield. When you inherit a project or join a new team, ChatGPT can act as an explainer. It’s increasingly used to summarize complex codebases, untangle architecture diagrams, or translate technical docs into plain English.
Common asks:
- “What is this service doing overall?”
- “Summarize this file and tell me its main responsibility.”
- “Explain this microservice’s architecture in 3 bullet points.”
Real example:
“I just joined a company that uses a service called ‘GraphFusion.’ I pasted its GraphQL schema and asked: what’s this service doing and who uses it?”
ChatGPT identified it as an API gateway and inferred data relationships faster than the dev’s onboarding buddy.
Why it works:
- Summarization is ChatGPT’s superpower
- Ideal for onboarding, knowledge transfer, and documentation
- Helps you “build a map” before diving into code
Dev Tip: Pair this with a local code indexing tool like Continue or Cursor IDE to feed larger context chunks.
Pattern 6: Acting as a coding mentor or reviewer
One of the most creative uses is simulated mentorship. Developers especially juniors or solo hackers are asking ChatGPT to play the role of a reviewer, interviewer, or pair-programming partner.
Common asks:
- “Review this function like a senior engineer.”
- “What questions would you ask if I submitted this in an interview?”
- “Act like a tech lead and give me feedback.”
Real example:
A bootcamp grad prepping for a backend interview used ChatGPT to simulate system design Q&A:
“Ask me a system design question, listen to my answer, and then critique it.”
The result? Sharper articulation, deeper confidence, and clarity on weak points, all without scheduling mock interviews.
Why it works:
- Feedback at scale: no need to wait on a teammate
- Simulates pressure situations (if you prompt it well)
- Gives structure to otherwise chaotic learning
Dev Tip: Use role-play prompts like “Pretend you’re a strict reviewer at Google” for high-quality simulated pressure.
Pattern 7: Comment generation and doc writing
You’ve probably seen this one in action: devs using ChatGPT to write docstrings, function comments, and even README scaffolds. But the real power is when they use it to translate intention turning dense logic into accessible summaries.
Common asks:
- “Add inline comments explaining each step.”
- “Write a docstring with param and return types.”
- “Summarize this module for the README.”
Real example:
One dev pasted a recursive tree traversal in Rust with no comments and asked:
“Explain each block of logic in simple terms for a junior dev.”
The result? A fully commented walkthrough with annotations that doubled as onboarding material.
Why it works:
- Converts opaque logic into human-friendly summaries
- Accelerates documentation debt repayment
- Ideal for unfamiliar codebases or legacy modules
Dev Tip: Ask “Would this make sense to a junior dev?” as a doc quality check.
Pattern 8: Project-specific helper bots
This is where devs get creative. From one-off CLI tools to Slack-integrated prompt bots, some teams are building mini copilots that know their stack. It’s the beginning of embedded AI.
Common asks:
- “Create a CLI wrapper that asks ChatGPT for code reviews.”
- “Build a Slack bot that answers dev questions from our README.”
- “Generate a JSON schema validator from a prompt.”
Real example:
A dev created a VS Code command that sends the current file to GPT and auto-generates tests, commit messages, and changelogs depending on the trigger keyword.
Another team trained a small RAG (retrieval-augmented generation) bot on their API docs to answer support tickets faster.
Why it works:
- Makes ChatGPT context-aware
- Automates repetitive workflows
- Gives AI superpowers inside your toolchain
Dev Tip: Explore tools like LangChain, LlamaIndex, and Continue for bootstrapping this.
Pattern 9: Research assistant and search replacement
More and more devs are skipping Google entirely. Instead of wading through SEO traps, they’re prompting ChatGPT directly sometimes pasting in links or terminal output to combine context.
Common asks:
- “Explain this error and suggest fixes.”
- “Compare OAuth and OIDC in under 100 words.”
- “What’s the difference between a goroutine and a thread?”
Real example:
Instead of clicking through 5 Stack Overflow tabs, a dev copy-pasted a panic stack trace and asked:
“What’s going wrong and what log line should I check first?”
The model not only suggested the issue it recommended adding a debug print 2 lines above the crash.
Why it works:
- Reduces friction in knowledge lookup
- Compresses 20-minute searches into 2-minute sessions
- Ideal when paired with technical follow-up prompts
Dev Tip: Don’t just ask “what is X” — add “compare X and Y with examples” for richer answers.
Closing thoughts: real use cases > prompt gimmicks
These 9 patterns aren’t hypothetical. They come from how real developers are working today — across side projects, teams, and production codebases.
Some use ChatGPT to code faster. But the ones getting the most value? They’re using it to think more clearly, get feedback sooner, and remove friction from the stuff they’d rather not do manually.
This isn’t just AI assistance, it’s cognitive scaffolding.
You’re not outsourcing thinking. You’re building better thinking loops.
What’s next in the series
In Part 2, we go deeper.
We’ll explore the habits that separate “prompt users” from actual AI-assisted devs. You’ll learn how high-performing engineers are designing feedback loops, crafting reusable templates, and building systems around their AI usage, not just clever one-liners.
Stay in the loop
This is just the beginning.
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