Vibe Coding with Sora: AI Prompt Engineering Tutorial
Introduction "Vibe coding" is the art of describing what you want in plain English and letting an AI model write the code for you. Instead of wrestling with syn...
Introduction
"Vibe coding" is the art of describing what you want in plain English and letting an AI model write the code for you. Instead of wrestling with syntax, you focus on intent—the vibe of the software you're trying to build—and the AI handles the boilerplate. It's a genuine shift in how software gets made, and it's changing everything from weekend prototypes to production workflows.
This tutorial is for anyone curious about AI-assisted programming. Beginners will find a complete, step-by-step path from zero to a working project. Experienced developers will find advanced techniques—chain-of-thought prompting, repo-level context, and refactoring workflows—that squeeze real productivity out of these tools.
By the end of this guide, you'll understand how AI coding assistants think, how to write prompts that produce reliable code, and how to build, test, and harden a small to-do application using vibe coding principles. Let's get started.
1. Understanding Vibe Coding and AI-Assisted Programming
1.1 What Is Vibe Coding? (Definition and Origins)
Vibe coding describes a workflow where you express intent in natural language and let an AI generate the implementation. The term captures a real cultural shift: from syntax-first development (where knowing the language is the bottleneck) to intent-first development (where knowing what you want is the bottleneck).
The origin story is simple. As large language models became good at code, developers noticed they could describe a feature conversationally and get working code back. The skill being tested moved from memorizing APIs to articulating requirements clearly.
1.2 How AI Coding Assistants Actually Work
Under the hood, tools like Sora, Copilot, Cursor, and Claude are powered by large language models. These models predict the next token—a chunk of text—based on everything in their context window (the text they can "see" at once).
Two consequences follow:
- Context is everything. If your prompt omits the framework, version, or existing code, the model guesses—and guesses wrong.
- Your prompt is the spec. The quality of your prompt directly determines the quality of your code.
1.3 When to Use Vibe Coding (and When Not To)
Great for:
- Prototypes and MVPs
- Boilerplate (config files, CRUD endpoints, tests)
- Refactoring and renaming
- Learning unfamiliar APIs
Use caution for:
- Security-critical code (auth, crypto, payment logic)
- Complex system architecture
- Production code shipped without human review
1.4 Setting Up Your Environment
Pick a tool that matches your workflow:
| Tool | Best For | |------|----------| | Sora | Conversational generation, creative + code | | GitHub Copilot | Inline suggestions in your editor | | Cursor | Repo-level context and multi-file edits | | Claude | Long-context reasoning and review |
Install your editor extension, add your API key, and try a first prompt:
Write a Python function that returns "hello world" and prints it.
If you get working code back, your environment is ready.
2. Prompt Engineering Fundamentals for Code Generation
2.1 The Anatomy of a Great Coding Prompt
A strong coding prompt has five parts: role, context, task, constraints, and output format.
Weak prompt:
Write a login function.
Strong prompt:
You are a senior Python developer.
Context: We use FastAPI and SQLAlchemy with PostgreSQL.
Task: Write an async login endpoint that verifies a bcrypt password hash.
Constraints: Use Pydantic for input validation; return HTTP 401 on failure.
Output: A single code block with imports and type hints.
The second prompt leaves almost nothing to guesswork.
2.2 Specifying Language, Framework, and Versions
"Write a function" fails because the model must invent a language. "Write a Python 3.11 function using FastAPI" succeeds because the constraints are explicit. Always pin versions:
Use Python 3.11, FastAPI 0.110, and Pydantic v2.
Do not use any package outside this list.
That last line is your best defense against hallucinated packages—libraries that don't exist but look plausible.
2.3 Providing Context: Files, Snippets, and Examples
Paste in relevant existing code so the model matches your conventions:
Here is my existing User model:
class User(BaseModel):
id: int
email: str
Now add a `create_user` function that follows the same style.
For style locking, use few-shot examples—show two functions you like, then ask for a third in the same style.
2.4 Iterating on Prompts: The Feedback Loop
Vibe coding is a loop: prompt → run → read error → refine. Suppose the AI generates:
from fastapi import FastAPI
app = FastAPI()
@app.get("/items")
def get_items():
return db.query(Item).all()
You run it and get NameError: name 'db' is not defined. Don't start over—refine:
The code fails because `db` is undefined. We use a SQLAlchemy
session injected via Depends(get_db). Rewrite the endpoint to
use that pattern.
Small, targeted corrections beat full rewrites every time.
3. Step-by-Step: Your First Vibe Coding Project
3.1 Step 1 — Defining the Goal in Plain English
Before touching the AI, write a one-paragraph spec:
Build a to-do app backend with FastAPI and SQLite. Users can create, list, update, and delete tasks. Each task has a title, optional description, and a
completedboolean. Include tests.
Turn vague ideas into testable requirements. "Make it nice" is not testable; "return 404 for missing tasks" is.
3.2 Step 2 — Generating the Initial Scaffold
Create a FastAPI project structure for a to-do app with:
- app/main.py
- app/models.py
- app/schemas.py
- app/database.py
- tests/
Use SQLite and SQLAlchemy 2.0. Include a requirements.txt.
Review the output. Check that files are separated logically and imports make sense before moving on.
3.3 Step 3 — Building Features Iteratively
Add one feature per prompt. To add a "mark as complete" endpoint:
Add a PATCH /tasks/{task_id}/complete endpoint that sets
completed=True. Return 404 if the task doesn't exist.
Follow the style of the existing endpoints.
@app.patch("/tasks/{task_id}/complete")
def complete_task(task_id: int, db: Session = Depends(get_db)):
task = db.get(Task, task_id)
if not task:
raise HTTPException(status_code=404, detail="Task not found")
task.completed = True
db.commit()
db.refresh(task)
return task
Small changes are easier to review and easier to revert.
3.4 Step 4 — Testing, Reviewing, and Hardening
Ask the AI to write its own tests:
Write pytest tests for the complete_task endpoint covering
success, 404, and idempotency.
Then run a manual review checklist before committing:
- [ ] Do all imports resolve to real packages?
- [ ] Are edge cases (empty input, missing IDs) handled?
- [ ] Any hardcoded secrets or credentials?
- [ ] Does the code match project conventions?
4. Advanced Vibe Coding Techniques
4.1 Chain-of-Thought and "Think Step by Step" Prompting
Ask the AI to plan before it codes:
Think step by step. First produce a short design doc covering
data model, endpoints, and error handling. Wait for my approval
before writing any code.
This two-phase approach catches design flaws before they become code you have to untangle.
4.2 Multi-File and Repo-Level Prompting
Tools like Cursor and Aider index your whole codebase, letting you prompt at the repo level:
Refactor the Task model to use a UUID primary key. Update all
references across app/ and tests/.
Without codebase indexing, paste the relevant files manually.
4.3 Prompting for Refactoring and Code Review
Review this module for security issues, naming problems, and
duplication. Suggest fixes as a diff.
AI review is excellent at catching dead code, inconsistent naming, and obvious injection risks—but treat it as a first pass, not a final verdict.
4.4 Generating Tests, Docs, and CI Pipelines
Write a GitHub Actions workflow that runs pytest on Python 3.11
and lints with ruff.
Generate a README with setup instructions, an API endpoint
table, and a curl example for each route.
These prompts turn generated code into a maintainable project.
5. Productivity Tips and Workflow Integration
5.1 Build a Personal Prompt Library
Save prompts that work. Organize by language and task:
prompts/
python/
fastapi-endpoint.md
pytest-suite.md
frontend/
react-component.md
Reusing a proven prompt beats reinventing it every time.
5.2 Keyboard-First Workflows and Snippets
Bind your AI tool to a shortcut, use inline prompts in your editor, and consider voice input for longer descriptions. Every context switch costs focus.
5.3 Knowing When to Stop Prompting and Start Typing
Apply the 80/20 rule: if two or three prompts haven't produced what you need, just write it yourself. "Prompt paralysis"—endlessly rephrasing instead of coding—is a real trap.
5.4 Measuring Your Speed Gains
Track time-to-prototype and bug rate in a simple dev journal:
| Date | Task | Time | Bugs Found | |------|------|------|------------| | 6/01 | CRUD endpoints | 25 min | 1 | | 6/02 | Auth flow | 90 min | 4 |
Patterns will emerge: some tasks are AI-friendly, others aren't.
6. Common Pitfalls and How to Avoid Them
6.1 Hallucinated APIs and Outdated Libraries
Models invent plausible-sounding packages. Always verify imports, check version numbers, and pin dependencies in your prompt.
6.2 Security and Licensing Risks
- Never paste secrets, keys, or customer data into prompts.
- Check generated code for license contamination—especially copy-pasted snippets from unknown sources.
6.3 Over-Reliance and Skill Atrophy
If you can't read the code, you can't debug it. Keep practicing fundamentals, review generated code critically, and treat AI as a collaborator—not a replacement for understanding.
Conclusion
Vibe coding isn't about abandoning craftsmanship—it's about redirecting it. The syntax-first era rewarded memorization; the intent-first era rewards clarity, specification, and review discipline. You've now seen the full arc: how these models work, how to structure prompts, how to build a project iteratively, and how to avoid the traps that catch beginners.
Start small. Pick one repetitive task this week—a test file, a config, a CRUD endpoint—and vibe-code it. Refine your prompt until it sings, then save it to your library. Over time, you'll build a personal toolkit that makes you dramatically faster without sacrificing quality. The best prompt engineers aren't the ones who type the most—they're the ones who know exactly what they want and can say it precisely.