The Last 30%: Where AI Coding Needs You Most

30 Jul, 2025

AI gets you started — but finishing is still your job.

In the age of generative coding tools like Copilot, GPT-4, and Replit Ghostwriter, developers are faster than ever. But there’s a trap hidden in that speed: the illusion of completion.

We call it the 70% Problem — the idea that AI can write 70% of the code, but the remaining 30% is where:

• complexity creeps in,
• product context matters most, and
• bugs love to hide.

⚙️ Where the 70% Works

LLMs are brilliant at:

• Generating UI components quickly (e.g., React forms)
• Writing scaffolding code, like REST endpoints or DTOs
• Translating specs into first-pass implementations
• Repetitive code patterns, boilerplate, migrations

This is productivity gold — the stuff devs would rather not handwrite anyway.

🧱 Where the 30% Bites Back

But the last mile always matters more:

• Handling business logic edge cases
• Integrating with messy legacy systems
• Performance tuning
• Writing accurate tests for production behavior
• Refactoring code to be clean, not just correct

AI can hallucinate. But worse — it overconfidently completes what it doesn’t understand.

The 70% can feel like 100% until review time.

And here’s the catch: that final 30% often has to fit inside the spaghetti structure created by the AI’s first pass.

So not only are you finishing the job — you’re untangling it as you go.

🕵️ Real-World Friction: A Mini Case Study

Imagine you ask your LLM:

"Create an API that returns products on discount for logged-in users."

You get a working Express.js endpoint. Nice!

But when you test it:

• It doesn’t account for user roles
• Discounts apply globally, not per user
• Performance tanks with large catalogs

The LLM nailed the structure — but missed the domain nuance.

That’s the 30% in action.

And now, that 30% has to be woven into an architecture you didn’t design.

🔄 Shift the Mindset: Draft, Then Engineer

If you treat LLMs as draft partners, not finishers:

• You’ll architect before prompting
• You’ll validate assumptions before merging
• You’ll know where to intervene — and why

💡 Pro tip:

Add a comment above AI-generated code like: // Prompted draft — verify edge cases + structure

This keeps reviewers sharp and expectations honest.

📌 Design for the 30%

The future isn’t just smarter AI — it’s smarter collaboration.

Consider designing your workflows to embrace this gap:

• Break problems into smaller, verifiable units
• Use tests as prompts and validators
• Automate the boring; engineer the brittle

AI is a power tool, not a magic wand. It shines within constraints.

🚀 Final Thought

We don’t just need faster code. We need better engineering judgment.

Understanding the 70% Problem lets you:

• Use AI confidently, without blind trust
• Focus human time where it matters most
• Build with speed and integrity

Tags: ai-assisted coding, software engineering, llms, productivity, code review, agentic systems