The Smarter Way of Using AI in Programming

Author:Murphy  |  View: 20319  |  Time: 2025-03-23 11:37:42

_Stuck behind a paywall? Read for Free!_

Since ChatGPT's launch in 2022, developers have split into two camps: those who ban AI in coding and those who embrace it. **** Many programmers, especially the more seasoned ones, not only don't use AI-generated code but also prohibit their teams from doing so. Their argument against using AI for Programming is understandable, "AI-generated code is unreliable."

Now even if you don't approve of this anti-AI-code stance, you must have faced challenges, hurdles, or hiccups when using AI for programming. The key is to find strategies to use AI to your advantage the right way.

Many of us are using a simple, but outdated AI strategy that was only good two years ago. We are cutting a tree down with kitchen knives.

I will begin this article with two major issues when we talk about AI for developers, and I will propose four reliable AI solutions that will separate you from the average programmer when it comes to using LLMs, no matter what kind of developer you are.

Table of Contents

· the wrong way you may be using AI… ∘ you're not using the right interface ∘ you have unrealistic expectations of AI · Cursor: AI-first IDE · Micro Agent: Code + Test Case · SWE-agent: AI against Github Issues · AI Commits: git commit -m · Let's Connect! · Further Reads

the wrong way you may be using AI…

… can be divided in two:

Image created by GPT-4o.

you're not using the right interface

When ChatGPT was released, for months, the mainstream method of working with AI was to go to the website and chat with gpt-3.5 in the browser. The AI scenery was simple.

You wanted ChatGPT to write docstrings and comment on your function?

  • You would copy that piece of code from your IDE,
  • give it to ChatGPT, while writing a lazy prompt, like"add comments",
  • it would spit back a new code,
  • then, you would make sure it didn't make the code faulty,
  • and if everything was fine, you would paste the new code into your IDE.

Many are still using AI to code the same way. I'm seeing this in students, programmers, and especially beginners. As the AI landscape has changed in the last two years, our methods for using AI have not adjusted. We haven't caught up to its full potential.

Diagram By Author.

you have unrealistic expectations of AI

The second pitfall of using AI is how we tend to use it. Typically we would ask the LLM to code something, we test if it works, and if it doesn't, we go back and forth manually to fix the issue. If we're lucky, we don't fall into an endless loop of hallucinations when we try to have the LLM understand what's wrong with the code and it doesn't.

We know it doesn't work, but for a lack of a better solution, we push and push it. It's easy to get frustrated this way.

Having said this, I will share four tools that can boost your programming productivity with AI.

Cursor: AI-first IDE

Tags: Artificial Intelligence Data Science Large Language Models Programming Technology

Comment

Recommend

  • ◦ How to mask PII data with FPE using Azure Synapse
  • ◦ How to Create Your Own CV Dataset Using Satellite Imagery: Wildfires from Space
  • ◦ Retrieval-Augmented Generation (RAG): From Theory to LangChain Implementation
  • ◦ Plotly and Python: Creating Interactive Heatmaps for Petrophysical & Geological Data
  • ◦ 17 (Advanced) RAG Techniques to Turn Your LLM App Prototype into a Production-Ready Solution
  • ◦ Urban Resilience: Tirana, a Case Study [Part 1]
  • ◦ Show Streamlit Apps in your Medium Blog
  • ◦ Harnessing Multi-Core Power with Asyncio in Python
  • ◦ Variance Reduction in Experiments – Part 1: Intuition
  • ◦ End-to-End Data Engineering System on Real Data with Kafka, Spark, Airflow, Postgres, and Docker
  • ◦ LLMs and Transformers from Scratch: the Decoder
  • ◦ Leveraging Gemini-1.5-Pro-Latest for Smarter Eating