AI Vibe Coding Done Right: Prompt Engineering, Cursor, and Automated Code Reviews

Every developer has had this experience: you describe what you want to an AI model, it generates something that looks right, you run it, and something breaks in a way you didn’t expect. You iterate. It breaks differently. You iterate again. Twenty minutes later you’re not sure if the AI saved you time or cost you time. The tool isn’t the problem. The approach is.

AI coding tools have moved from novelty to standard infrastructure faster than most workflows have adapted to them. According to Stack Overflow’s 2025 Developer Survey, 81,7% of developers now use Chat GPT and 67,9% GitHub Copilot as coding assistants. But adoption and effective use are two very different things.

The developers who consistently get production-quality output from AI aren’t prompting differently in some magical way. They’ve built workflows that make AI reliable: managing context carefully, giving the model precise instructions before it writes anything, and knowing when to use which tool for which part of the job. This post covers exactly that, from the fundamentals of prompt engineering for developers to a practical AI vibe coding setup with Cursor, plan mode, and automated code reviews.

What Is AI Vibe Coding and Why Most Developers Are Doing It Wrong

The term AI vibe coding was coined by Andrej Karpathy, former AI lead at Anthropic and researcher at OpenAI, to describe a paradigm shift where developers stop writing most of the code themselves and instead focus on ideation, validation, and review. The AI handles the repetitive, boilerplate, and even moderately complex parts. The developer guides, directs, and verifies.

The concept has caught on fast. According to Stack Overflow, 92.6% of developers now use an AI coding assistant at least once a month, and adoption keeps accelerating. The problem is that most developers interact with AI the same way they’d google something: vague, unstructured, hoping for the best. That approach produces generic output.

The difference between a developer who gets consistent, production-quality results from AI and one who spends half their time fixing hallucinations comes down to one thing: how deliberately they manage context and prompts.

Why Context Management in LLMs Is the Foundation of Good AI Coding

Before getting into tools and workflows, it’s worth understanding why context matters so much at a technical level.

LLMs are probabilistic models. When you ask a model something, it doesn’t retrieve a fixed answer, it generates the most probable response given your input. If your input is vague or ambiguous, the model doesn’t fail silently; it confidently generates something plausible that may be completely wrong for your use case.

There’s a common misconception that simply giving the model more context always helps. Research shows otherwise: models struggle with very large context windows, and key information buried in a long prompt often gets deprioritized. The goal is precise, structured context. Think of it less like filling a container and more like briefing a collaborator: give them what they need, in a clear format, and leave out the noise.

For context management in LLM-based coding workflows, this means:

• Being explicit about what you want, not what you don’t want.
• Breaking down complex tasks instead of dumping everything in one prompt.
• Keeping chat sessions focused, one session per feature or concern.
• Using structured guidelines that the model reads before generating anything.

This is the core of what experienced developers call guided coding or structured coding with AI, and it’s what separates developers who get real leverage from AI from those who don’t.

Guided Coding: A More Precise Take on AI Vibe Coding

The term vibe coding implies a certain looseness, just vibe with the AI and see what comes out. In practice, the developers getting the best results are doing something more deliberate. A better name for it is guided coding: you’re still letting the AI do the heavy lifting, but you’re providing structured guidance before it writes a single line.

The shift in role is real. As a developer doing AI vibe coding, you’re no longer primarily a code writer. You’re a problem describer, a plan reviewer, and a quality validator. That’s a fundamentally different skill set, and it’s one worth developing intentionally.

Some concrete practices that define guided coding:

• Split problems into small, focused tasks. If you need to implement three API endpoints, don’t ask for all three at once. Do one, validate it, then move to the next. The model produces better output when the scope is narrow and the expected result is clear.
• Use precise vocabulary. Terms like “best practices” or “clean code” are too vague, the model will interpret them generically. Instead, link to specific references: a style guide, a design pattern, a concrete example from your codebase. When you say “follow the factory pattern as defined here,” you get a very different result than “use best practices.” As a bonus, getting the model to reason about the right implementation path before writing code also uses significantly fewer tokens, which means lower costs.
• Tell the model how to run the code. If you don’t specify the execution command, models frequently get it wrong on the first try and waste tokens figuring it out. One line in your guidelines, run tests with pytest -v, eliminates that entire failure mode. A practical way to do this at scale is to set up a CLAUDE.md file (if you’re using Claude Code) specifying the project dependencies, stack, and how to run scripts. The model will read it at the start of every session and apply those instructions consistently.
• Use conversational history strategically. If you’re building on a feature you discussed earlier in the session, stay in the same chat. The model has that context and will produce more consistent results. Start a new session when you switch to a completely different concern. That said, watch your context size: longer chats cost more tokens, and therefore more money. Claude Code, for example, surfaces session statistics so you can monitor usage and decide when to clean up a session before it gets too large.

Setting Up Cursor for Serious Development

Cursor is the IDE that most developers gravitate toward when they start taking AI vibe coding seriously. It’s a VS Code fork built specifically for AI-assisted development. The reason it exists as a fork rather than a plugin is that VS Code’s extension architecture is too restrictive to support the kind of deep IDE-level AI integration that makes a real difference. Starting from the VS Code codebase also means you get the entire plugin ecosystem from day one, migration from VS Code is essentially frictionless.

That said, Cursor isn’t the only option. Several strong AI coding assistants, including Claude Code, GitHub Copilot, and others, aren’t IDE-dependent at all, and work equally well regardless of your editor of choice. The principles in this post apply across tools; pick what fits your workflow.

The features that matter most for AI in frontend development and backend work alike:

• Autocomplete that understands your codebase. Not generic suggestions. Make suggestions grounded in what you’re actually building. Most developers who switch from GitHub Copilot to Cursor notice this difference immediately.
• Multiple modes for different tasks. Ask mode for understanding existing code. Agent mode for autonomous multi-step work. Plan mode for complex features where you want a structured approach before touching the codebase.
• Model switching. You can pick different models for different tasks within the same project. This matters more than most developers realize, and it directly affects both output quality and cost.

Cursor Rules: The Most Underused Feature

Cursor rules are project-specific guidelines the model reads before generating code. They’re the most direct implementation of prompt engineering for developers. Instead of re-explaining your standards in every prompt, you define them once and the model follows them consistently.

Rules can be scoped three ways: applied globally to every session, applied intelligently by the model based on what you’re working on, or applied to specific file paths (useful when you have different conventions for frontend and backend). The community maintains a public directory of rules at cursor.directory if you need a starting point or want inspiration.

What’s worth putting in your rules:

• How to run tests and execute files (eliminates a very common failure mode).
• Logging preferences, models default to print statements everywhere; specify your logging library.
• Whether to add inline comments or not (models over-comment by default).
• Commit message format if you want the model to generate PRs.
• References to specific design patterns your project uses, with examples.

A practical way to think about building your rules: take 30 minutes and write down everything that annoys you when reviewing AI-generated code. Each of those annoyances is a rule. Over time, your rules file becomes a precise spec of how you want to work and the model follows it.

Plan Mode: The Feature That Changes How You Use AI for Complex Work

Plan mode is one of Cursor’s most impactful features for agentic coding workflows. Before writing any code, the model reads your codebase, asks clarifying questions, and generates a step-by-step implementation plan. You review and adjust the plan. Only then does it start building.

Why this matters in practice: AI models are very good at producing plausible-looking plans that have subtle mistakes. In plan mode, those mistakes surface before they’re embedded in code, before you have to untangle five modified files to fix something that could have been caught in a two-minute plan review.

A typical plan mode workflow for a new feature:

1. Use a capable model (Opus, GPT-5, Codex or similar) for planning, this is where you want the most reasoning ability.
2. Let the model read the codebase and ask its clarifying questions. Answer them specifically.
3. Read the plan carefully. Look for assumptions the model made that don’t match your requirements.
4. Switch to a faster, cheaper model (Sonnet, Gemini Flash) for implementation, the hard thinking is already done.
5. Let the agent build, then validate.

The model switching step is worth emphasizing. Using an expensive model for implementation once the plan is set is wasteful, the implementation is largely mechanical at that point. Save the expensive model for planning, where nuanced reasoning actually matters. Cursor’s own research found that more experienced developers are more likely to plan before generating code, which aligns with what developers report anecdotally: planning mode produces significantly fewer revision cycles.

This workflow also handles one of the most common failure modes in AI-driven software development: the model confidently implementing something in a deprecated or incorrect way because it wasn’t given enough context upfront. The planning conversation surfaces those issues before they become code.

Automated Code Reviews with Claude Code

One of the highest-leverage applications of AI in a development workflow is automated code reviews. There are two complementary ways to set this up, and using both is a strong pattern:

• Local review before the PR. You can create a Claude Code skill that runs a code review locally, before the PR is even opened. This catches the most obvious issues fast, costs less than a full CI run, and lets you iterate on the feedback privately. The agent reviews your diff against your standards, flags problems, and you can address them before anyone else sees the code.
• Automated review on pull requests. Using Claude Code with GitHub, you create a YAML configuration file in your repository that specifies which model to use for reviews, what aspects to focus on (security, code style, design patterns, performance), and how to format the output,  for example, a final verdict of approved, changes requested, or needs discussion, followed by only the most critical and actionable recommendations. Once configured, every time a PR is opened, Claude Code is triggered automatically (via GitHub Actions) and posts a structured review as a comment. Note that you’ll need to set up your API key as a secret in your CI configuration for this to work. The review breaks the analysis into specific steps rather than generating one undifferentiated block of feedback.

The two-step approach: local skill for immediate feedback, PR-level review for team visibility,  is especially effective. Your team sees cleaner code, reviews take less time, and the developer gets a useful feedback loop well before the human review even starts. This is one of the core patterns in a mature AI workflow for developers.

The Limitations You’ll Actually Hit

Not every limitation is obvious until you hit it. Here are the ones that come up most consistently when working with AI vibe coding in real development workflows:

• Hallucinations on framework versions. AI models have a training cutoff. When working with newer SDKs, recently released APIs, or frameworks that changed significantly in the last year, models will sometimes generate code using deprecated patterns, confidently. One of the best mitigations is Context7, an MCP server that gives your model access to up-to-date documentation at query time. It’s one of the few MCPs worth adding to your setup.
• Loops on unsolvable problems. LLMs are trained to solve problems. If you ask for something that isn’t actually possible: a library feature that doesn’t exist, an integration that isn’t supported, the model will keep trying instead of telling you it can’t be done. Plan mode is better at surfacing these dead ends early. When you’re deep in agent mode and something keeps failing in the same way, check the official documentation before spending more tokens.
• Context window degradation.Long sessions produce worse output, and cost more. The model doesn’t “remember” the beginning of a very long conversation with the same fidelity as the recent messages, and every token in that long history counts toward your session costs. For complex features, start a new chat when the previous one has run its course.
• Cost management. Token costs add up quickly with agentic workflows, especially when using expensive models for everything. The practical approach: use the most capable model for planning and architecture, switch to a faster model for implementation, and use /summarize or /recap (which summarizes what the model has built so far in the session) to compress context, or start a new session when a chat gets long. The difference between a disciplined model-switching approach and a careless one can be significant over a week of active development.
• Your fundamentals still matter. This is worth saying directly: code performance and architecture judgment don’t come from AI, they come from you. Developers who don’t understand what the AI is generating will eventually hit a wall they can’t get past. AI accelerates good engineers. It amplifies the problems of developers who are using it to skip the learning they haven’t done yet.

Privacy and Security: What You’re Actually Agreeing To

When you use cloud-based AI coding tools, your code goes to the provider’s servers. That’s how the model sees your context. For most open-source and personal projects, this is a reasonable tradeoff. For proprietary business logic, client code, or anything with compliance requirements, it deserves explicit consideration.

The practical options: use a local model (Ollama and similar tools make this accessible), configure your tool to use a self-hosted or private deployment, or establish team policies about what can and can’t be shared with AI tools. Some developers use local models specifically for sensitive commit context, like generating PR descriptions from proprietary code, while using cloud models for everything else.

What This Means for How You Work

The shift that AI vibe coding represents isn’t about writing less code. It’s about spending more of your time on the parts of engineering that require human judgment: problem framing, architecture, review; and less on the parts that are mechanical. That’s a good trade, but it requires investing in the skills that make AI useful: clear communication, structured thinking, and the ability to evaluate what the model produces.

The developers who get the most out of these tools are the ones who’ve built workflows that make it easy to catch mistakes early, validate outputs quickly, and iterate without accumulating technical debt. By the end of 2025, roughly 85% of developers regularly use AI tools for coding, the gap is no longer between developers who use AI and those who don’t. It’s between developers who use it well and those who don’t.

If you want to go deeper on engineering with AI beyond the tooling layer, the fundamentals of how to structure AI-assisted work apply across stacks and tools.

Work on Challenges Like This at BEON.tech

At BEON.tech, our engineers work on real, complex problems across a range of US tech companies:

• Building AI-assisted workflows,
• Integrating agentic tools into production codebases, and
• Pushing the boundaries of what modern development teams can ship.

If you’re a software engineer in LATAM who wants to work at the frontier of AI-driven development, with teams that take these tools seriously and projects that actually challenge you, explore what working at BEON.tech looks like.

FAQs

What is guided coding and how is it different from vibe coding?

Guided coding is a more structured version of vibe coding. Instead of prompting loosely and hoping for good output, you provide precise context: through rules files, explicit instructions, and structured prompts, before the model generates anything. The output is more consistent and requires fewer revision cycles.

Is it safe to use AI vibe coding tools with proprietary code?

Your code goes to the AI provider’s servers when using cloud-based tools. For open-source and personal projects, this is usually an acceptable tradeoff. For proprietary business logic or compliance-sensitive code, consider local models, private deployments, or team policies about what can be shared.

What are Cursor rules and how do I set them up?

Cursor rules are project-level guidelines stored in your repository that the model reads before generating code. They define things like coding standards, logging preferences, test execution commands, and design patterns. You can scope them globally, to specific file paths, or let the model decide when to apply them. The community directory at cursor.directory has a large collection of examples.

How does plan mode in Cursor work?

In plan mode, the model reads your codebase, asks clarifying questions, and generates a step-by-step implementation plan before writing any code. You review and adjust the plan, making edits freely before approving it, and only then does the agent begin building. This surfaces assumptions and errors before they’re embedded in code, reducing revision cycles significantly.

How do I set up automated code reviews with Claude Code?

Create a YAML configuration file in your GitHub repository that specifies the model, what aspects to review, and how to format the output. You’ll also need to add your Anthropic API key as a secret in your CI setup (e.g: GitHub Actions) so Claude Code can authenticate and run. Once configured, it triggers automatically on every pull request and posts a structured review as a comment. The full setup takes about 15 minutes and works with any model available through the Anthropic API.