How LLMs Actually Improve Engineering Productivity

Large language models have reshaped how software gets built. But the marketing noise makes it hard to separate reality from hype. After two years of shipping production software with LLM coding agents at RG INSYS, here’s what actually improves, and what doesn’t.

Three Tools, Three Roles

Claude Code, Cursor, and GitHub Copilot serve different purposes. Using them together is where you see real gains.

Claude Code excels at multi file implementations. You describe a feature, “auth + CRUD API for job postings with tests”, and it generates routes, models, middleware, and test files in one pass. It reads your codebase, infers conventions, and produces coherent output. We use it for greenfield modules, scaffolding, and large refactors. Typical task: 2–3 developer days compressed into 30–60 minutes, followed by human review.

Cursor is built for real time pair programming. You work in context: ask for edits, fixes, or explanations while coding. It’s ideal for iterating on existing code, debugging, and extending features without leaving the editor. Junior developers benefit most, they get instant help on patterns and APIs. Seniors use it to offload boilerplate and focus on architecture.

GitHub Copilot is the lightest touch: inline completions as you type. Best for routine code: loops, conditionals, error handling, repetitive structures. No context switching. It’s the baseline tool, we expect every engineer to have it on. Gains are incremental but consistent across the board.

What Stays Human

AI does not replace design. Architecture decisions, service boundaries, data models, API contracts, scaling strategy, remain human work. Models can suggest patterns, but they don’t understand your business, constraints, or long term roadmap.

Security is non negotiable. Auth flows, input validation, encryption, and permission checks must be reviewed by engineers. AI can generate plausible code that looks correct but has subtle vulnerabilities. We never auto merge security critical paths.

Business logic is another human preserve. Complex rules, edge cases, and domain specific behaviour need someone who understands the problem. AI can scaffold and suggest, but the final responsibility sits with the engineer.

Real Productivity Metrics

Our internal data (2024–2025) shows measurable gains on specific task types:

• CRUD and APIs: 3–4× faster. What used to take 2–3 days now takes a few hours, including review.
• Test generation: Unit tests, integration tests, and mocks, 4× faster. Coverage improves because writing tests is less tedious.
• Documentation: READMEs, API docs, inline comments, 3–4× faster. Consistency improves too.
• Refactoring: Renames, file moves, type updates, 2–3× faster. Less manual, fewer mistakes.

Where we don’t see meaningful gains: greenfield architecture, security design, and novel problem solving. Those still scale with seniority.

Common Misconceptions

“AI writes code, so engineers become optional.” Wrong. AI produces code; engineers produce correct, maintainable, secure systems. Review, integration, and accountability are human jobs.

“All three tools do the same thing.” They don’t. Claude Code handles full workflows. Cursor is for in editor collaboration. Copilot is for autocomplete. Using the wrong tool for the task wastes time.

“Productivity gains are uniform.” They’re not. Repetitive, well defined work accelerates most. Ambiguous, creative work accelerates least.

How AI Changes Team Composition

We’re seeing a shift toward smaller, more senior teams. One senior engineer with AI can often deliver what used to require 2–3 developers. Junior engineers are still valuable, they learn faster with AI assistance and can handle more ownership earlier. But the mix changes: fewer mid level "implementation only" roles, more architects and reviewers.

Communication and product sense matter more. Engineers who can decompose problems, write clear prompts, and validate AI output are in higher demand than those who only code from specs.

The Practical Takeaway

Use Claude Code for multi file tasks. Use Cursor for real time help. Use Copilot for inline completions. Keep architecture, security, and business logic firmly in human hands. Expect 3–4× gains on CRUD, tests, and docs, not across everything. Invest in review processes and prompt quality. AI powered software engineering is real, but it rewards discipline and clear expectations, not hype.

The teams that succeed are the ones that treat AI as a force multiplier, not a replacement. Define what "done" means. Review everything. Ship faster, but ship correctly. At RG INSYS, we’ve seen the numbers play out across dozens of projects, and the engineers who combine strong fundamentals with effective AI use are the ones delivering the most value.