Why Code Is More Precise Than Natural Language

We often treat programming languages like they’re harder to “speak” than natural language. But if you look closely, the opposite is true.

Code is actually a simplified version of language — and far more specific. That’s why writing code can sometimes be the most direct way to describe what you want to build.

Step 1 — Simple math

Code (concise & exact)

// Multiply a and b, then divide by 4
const a = 2, b = 2.5;
const c = (a * b) / 4; // ? 1.25

Natural language

Take two quantities: the first is exactly two, the second is exactly two and one-half. Multiply the first by the second, then divide the result by four. The outcome is precisely one and one-quarter.

Step 2 — Validation + rounding

Code (tiny but rule-rich)

// a must be a whole number, b must be finite, round result down
const a = 2, b = 2.5;
if (!Number.isInteger(a) || !Number.isFinite(b)) throw new TypeError("Invalid inputs");
const c = Math.floor((a * b) / 4); // ? 1

Natural language

Take two quantities: the first is exactly two and must be a whole number, the second is exactly two and one-half and must be a real, finite number. If either value fails to meet these requirements, stop immediately and reject the inputs. Multiply the first by the second, then divide the result by four, evaluating in that order. Once you have this value, round it down to the nearest whole number, discarding any fractional part, even if the remainder is less than one-half. The outcome in this case is exactly one.

The takeaway

Adding a single if statement and a Math.floor call inserts multiple new rules with minimal extra syntax.

In natural language, the same change blows up into extra sentences, careful ordering, and explicit qualifiers. Programming languages pack surprising precision into symbols — tiny code changes require many additional words in plain English to preserve exactness.

From vibe coding to precise collaboration

When we “vibe code” — casually describing what we want to build — we skip the precision that makes code powerful. AI coding agents handle vibe coding, but perform best when given both instructions and constraints.

5 (well, 6) Steps to Work Smarter with AI Coding Agents

1. Define the Rules Once, Reuse Forever — Keep a permanent reference doc (e.g., agents.md or Cursor rules) with your style, architecture, naming, and testing conventions.

2. Break the Work into Atomic Steps — Request small, reviewable tasks instead of “build it all at once.” Smaller scope means less drift.

3. Use Code-Level Examples, Not Just Descriptions — Show the AI real code snippets to anchor your intent.

4. Enforce Guardrails Programmatically — Use linters, type checks, and automated tests so quality issues get caught immediately. Well defined type systems are key here.

5. Iterate with Feedback Loops — Review, comment, and update your rules doc as you spot recurring misunderstandings.

6. Review Code Commits Like Any Other Developer — For now, AI-generated code still needs a human safety net. Even if it passes tests and looks clean, it may hide subtle issues that hurt maintainability, performance, or security. Treat AI like a junior developer — review every commit before merging. This ensures you’re not introducing code that “works” today but causes headaches tomorrow.

This is just the surface. In future posts, I’ll break down each of these six steps with concrete examples so you can get the best out of your AI coding agents without drowning in vague, verbose prompts.