# Coding with LLMs is exposing a massive gap between hype and > Source: > Published: 2026-08-22 17:00:46+00:00 # Coding with LLMs is exposing a massive gap between hype and We recently started a pilot program at my company to integrate [Claude Code](/en/tags/claude%20code/) and GitHub Copilot into our core backend workflow. The goal was simple: see if we could accelerate our sprint velocity by offloading the "grunt work" to an AI workflow. ## The reality of the "grunt work" myth The biggest friction point we hit was the assumption that coding is mostly repetitive typing. It isn't. For a senior engineer, the actual "coding" part—the syntax, the brackets, the API calls—is the easy bit. The hard part is the reasoning: understanding how a change in the database schema will impact a downstream microservice three layers away, or predicting how a specific concurrency pattern will behave under a heavy load. When I use an LLM to generate a function, I spend more time auditing the logic than I would have spent just writing it from scratch. I have seen too many instances where the AI produces code that looks syntactically perfect but is logically catastrophic. It might use a deprecated library version, or worse, it might introduce a subtle race condition that only manifests in production. ## Where the tools actually add value It isn't all doom and gloom, though. We did find a sweet spot for deployment and boilerplate. If I need to write a unit test suite for a very predictable utility function, or if I need to generate a complex RegEx pattern, the AI is incredible. It acts as a high-speed rubber duck. Here is a quick breakdown of our team's experience during the pilot: **Boilerplate Generation:** High efficiency. Great for scaffolding new modules or writing repetitive CRUD operations.**Debugging:** Mixed results. It's great at catching "silly" syntax errors, but struggles with deep architectural flaws.**Documentation:** Very strong. It can take a messy function and wrap it in clean, standard Docstrings or JSDoc.**System Design:** Poor. It lacks the "big picture" context of our specific infrastructure and legacy constraints. ## The skill gap is widening There is a specific type of "AI-driven laziness" creeping into the workflow. When the tool provides an answer that is 90% correct, there is a massive temptation to just hit "Tab" and move on. This is where the danger lies. If you don't have the foundational knowledge to spot that missing 10% of error, you aren't a programmer anymore; you're just a glorified copy-paster. The better you are at programming, the more you see the hallucinations, the inefficiencies, and the logical leaps the model makes. To a beginner, it looks like an expert. To an expert, it looks like a very fast, very confident intern who occasionally lies to your face. We are moving toward a world where prompt engineering is a secondary skill, but deep, fundamental computer science remains the only way to actually ensure the system doesn't crash. [Next Our docs scored 67 on Vercel's new agent-readiness test →](/en/threads/7234/) [these real-world AI monetization case studies](https://tanyan888.com/), with plenty of directly applicable cases.