The impact of LLMs on problem-solving in software engineering

As artificial intelligence increasingly permeates the software engineering workflow, a critical conversation has emerged about its appropriate use in computer science problem-solving. LLMs offer powerful assistance for code generation and debugging, but their influence on the fundamental problem-solving skills that define engineering excellence presents a complex dilemma. Finding the right balance between leveraging AI tools and maintaining core technical competencies is becoming essential for the future development of both individual engineers and the field as a whole.

The big picture: Engineers are increasingly using Large Language Models to tackle computer science problems, raising questions about the long-term impact on problem-solving skills.

• LLMs can automate repetitive tasks, generate code snippets, assist with debugging, and help with brainstorming, freeing engineers to focus on more complex challenges.
• Despite their utility, LLMs have significant limitations including hallucinations, inconsistencies, and biases that require careful review of their outputs.

Key limitations: LLM training data primarily contains solutions to known problems, making these tools less reliable when confronting truly novel challenges.

• When engineers face familiar problems, LLMs can provide immediate solutions, but this convenience risks atrophying core problem-solving skills.
• The burden of detecting errors in LLM-generated solutions remains entirely with the engineer, requiring maintained expertise.

Why this matters: The current trend prioritizes speed over depth of understanding, potentially compromising engineers’ ability to tackle genuinely complex problems in the future.

• Unlike search engines that balance exploration and exploitation, LLMs encourage immediate exploitation of the first provided solution.
• Computer science developed to help humans solve problems faster, but with engineers increasingly relying on AI, the traditional mastery of algorithms is weakening.

Behind the numbers: The pressure to deliver quick solutions is driving increased reliance on AI tools at the expense of developing focused problem-solving abilities.

• The skill of focus, like any capability, requires consistent practice to maintain and improve.
• The trend could eventually lead to a future where complex problem-solving depends more on self-reflecting AI systems than human ingenuity.

The solution: Engineers should strive to understand the reasoning behind LLM-generated solutions rather than accepting them blindly.

• Balancing AI assistance with continued development of fundamental skills is essential for maintaining problem-solving capabilities.
• A focus on understanding the “why” behind solutions, not just the “what,” helps preserve critical thinking abilities that AI cannot replace.