How I Write Software with LLMs

A year ago, I would've laughed at the idea of an AI co-pilot writing production code. Today, I can't imagine shipping software without one. LLMs have fundamentally changed how I approach development—not by replacing thinking, but by amplifying it.

The New Development Workflow

My process now looks like this: I describe what I want to build in plain English, iterate with an LLM on the approach, generate a solid foundation, then refine and test. It sounds simple, but it's transformed my velocity. I spend less time on boilerplate and more time on architecture decisions.

The real magic isn't in perfect code generation. It's in the rapid feedback loop. Need to refactor that authentication module? Ask the LLM. Want to explore three different API designs? Generate them all and compare. This iterative dance with an AI partner has made me a better engineer because I'm constantly thinking at a higher level.

Where I Use LLMs Daily

Scaffolding: Getting initial project structure and boilerplate out of the way in seconds
Refactoring: Asking for suggestions on code clarity and performance improvements
Documentation: Auto-generating docstrings and API documentation that actually make sense
Test generation: Creating edge case tests I might've missed
Learning: Explaining unfamiliar libraries or patterns in my own terms

The Cost Question

Here's what stops many developers: API costs. Running LLM queries at scale feels expensive, even though per-request prices are dropping. You end up overthinking whether to ask the AI for help on that small task, which defeats the purpose.

That's where AiPayGen changed my workflow. It's a pay-per-use Claude API that strips away the monthly subscription tax. I only pay for what I actually use—perfect for developers experimenting with LLM-assisted development.

Quick Example: Using AiPayGen

Here's how simple it is to integrate into your development toolchain:

import requests
import json

def generate_code(prompt):
    response = requests.post(
        "https://api.aipaygen.com/v1/messages",
        headers={
            "x-api-key": "your_api_key",
            "content-type": "application/json"
        },
        json={
            "model": "claude-3-5-sonnet",
            "max_tokens": 2048,
            "messages": [
                {"role": "user", "content": prompt}
            ]
        }
    )
    return response.json()["content"][0]["text"]

# Generate a Python decorator
code = generate_code(
    "Write a Python decorator that caches function results with a 5-minute TTL"
)
print(code)

That's it. No complex setup. No surprise bills. You get Claude's power—the same model that produces state-of-the-art code—with straightforward pricing.

The Future of Development

Writing software with LLMs isn't about letting AI do the work. It's about raising the floor on code quality and development speed while you focus on decisions that matter. It's about asking "what if?" more often because the cost of exploration is negligible.

If you're curious about this workflow but hesitant about API costs eating into your project budget, AiPayGen removes that friction entirely.

Try it free at https://api.aipaygen.com — 10 calls/day, no credit card.