I’m planning to return to pure coding for now, stepping away from AI-driven assistants like Cursor, Windsurf, and their peers. These tools often spit out convoluted solutions for trivial features, turning ten-minute jobs into hours of debugging. When you ask the AI to fix its own mistakes, hallucinations multiply, and you end up deeper in the weeds. Today’s AI coding assistants are temperamental and far from reliable.
Before leaning on an AI, you must first understand the problem you’re solving and how you would solve it manually. Mainstream paid LLMs can generate solid code, if you steer them correctly, but they don’t innately grasp your unique vision. They excel at one-off, well-trod tasks (like scaffolding a basic to-do list) because countless examples exist in their training data. Custom ideas? They’ll guess at best and miss the mark at worst.
Instead of chasing one-shot demos, invest a few days in structured planning:
First, write a problem-statement document (Markdown or plain text) that crisply defines the issue, target users, constraints, and success criteria.
Next, draft a high-level architecture overview. Sketch major components, data flows, and directory structure, ASCII diagrams or Mermaid charts help. Link this overview from your problem statement.
Then, for each component, create a dedicated spec file. Detail inputs, outputs, interfaces, edge cases, and performance targets. Reference these files in your architecture doc so any agent knows exactly where to look.
Finally, assemble a tech-stack document listing your chosen languages, frameworks, libraries, and versions. Without it, the model will make default picks that may not suit your needs.
Armed with these artifacts, define your minimum viable product (MVP) by selecting core features and translating them into a tasks list (in Markdown). Each task must be the smallest independently deliverable unit, for example:
“File: src/utils/stringFormatter.js
Function: formatCamelCase(input: string) -> string
Behavior: Split words on non-alphanumeric characters, capitalize each segment, join without spaces
Acceptance: ‘helloWorld’ returns ‘HelloWorld’, ‘foo_bar’ returns ‘FooBar’.”
Feed the AI one task at a time. When it produces code, immediately write and run unit tests. Verify behavior, then update your tasks list: mark the task complete, append a changelog entry summarizing the change, and move on. This sprint-like cadence keeps scope tight and prevents the model from drifting into over-engineering.
When the AI inevitably misfires, supply concrete examples of desired versus undesired output. Few YouTubers show this level of discipline, they whip up demos, skip testing, and call it a day. Real builders invest in documentation, testing, and iteration; they don’t have time for fifteen-minute highlight reels.
Treat the LLM as an obedient assistant, not an autonomous engineer. It will follow your instructions exactly, no more, no less, but it won’t “learn” your codebase. Success depends on prompt quality. Invest time in mastering prompt engineering, start with the Google AI white paper by Lee Boonstra. Embed guardrails like “optimize for readability,” “limit dependencies,” or “produce functions under 30 lines” to constrain complexity.
Once you’ve honed your prompts and specs, you’ll see AI become a force multiplier. It churns out boilerplate, handles edge cases, and can even draft thousands of lines of unit tests overnight, work that would take you days by hand. Your role shifts to design, algorithm choice, and integration, the truly creative engineering tasks. Dependency conflicts and context-window limits remain hurdles, but they’re far more tolerable than manually typing every line of code.
In practice, breaking work down to individual functions delivers the best results. Describe the function’s behavior in precise computer-science terms, inputs, outputs, algorithmic steps, and let the model translate English into code. A detailed description yields code at PhD-level quality. The real bottleneck becomes your ability to articulate specs, not the AI’s coding prowess.
Over time, you’ll find that coding without AI feels like working with one hand tied behind your back. Many workplaces demand rapid delivery; writing thousands of lines manually in a week would be impossible. Yes, AI can sometimes over-engineer, counter that with smaller tasks, tighter rules, and clearer prompts. The return on investment is undeniable.
Ironically, early AI assistants felt magical, but in recent months heavy marketing hype has sullied many offerings. Some IDE integrations and model providers grew bloated, unreliable, and overpriced, digestive slime coating the industry. Service-side models and infrastructure platforms, however, continue to improve, with only a few bad actors charging absurd fees. Navigating the hype swamp takes care, but reliable paths still exist.
Curiously, many companies that have gone off the rails share traits: they’re staffed by folks with limited real-world engineering wisdom, often centered in San Francisco’s echo chamber (and sometimes NYC). Egos far outsize expertise, and they insist their flawed releases smell like roses, dare you disagree, and you’re branded a “hater.” A handful even blame foreign actors for their missteps, a telltale sign of an inbred echo chamber detached from reality.
Stripped of hype and fevered demos, the simple truth remains: AI excels at following crisp instructions, not at internalizing vague goals. By investing in planning docs, precise prompts, and incremental task breakdowns, you convert LLMs from unruly code monkeys into dependable development partners. Try this structured approach on your next project, and you’ll never look back to coding alone.