Plan First, Glue Second: A Three-Step Method That Stops AI from Writing Spaghetti Code
AI coding tools default to generating maximum code for minimal prompts, which produces unmaintainable output. A structured planning-and-glue workflow cuts that output down to the minimum viable integration code, making AI-generated projects reviewable, debuggable, and extensible rather than disposable prototypes.
Treating AI as a code generator produces bloated, hallucination-riddled output. A three-step method flips the relationship: force the model to output a detailed plan with explicit boundaries and data structures before writing any code, then instruct it to act as glue between mature open-source libraries instead of hand-rolling algorithms, and finally have the AI critique your own prompts to close ambiguity gaps. The result is concise, maintainable code where every component and data field is pre-negotiated.
Applied to a React ToDo list, the planning phase locked the tech stack, component tree, and field names before a single line was written. Adding drag-and-drop later took roughly twenty lines of glue code wrapping react-beautiful-dnd, with zero manual coordinate math. A post-hoc prompt review caught an unstable ID generation rule and replaced it with nanoid.
The core shift is from programmer-plus-generator to architect-plus-collaborator. Hard boundaries prevent feature creep, library-first glue reduces surface area for bugs, and meta-review turns prompt writing into a self-improving loop that works across any stack.
The method reframes AI from a code generator to a pair-programming colleague, which changes the developer's own behavior: they invest upfront in specifications instead of iterating on bad output.
Glue programming is a deliberate constraint that reduces the surface area for hallucinations; the AI is asked to research a library's API rather than invent one, which plays to its strength in pattern-matching existing documentation.
The meta-review step creates a feedback loop where prompt quality improves without requiring the developer to become a prompt-engineering expert; the AI surfaces its own failure modes.
Hard boundaries like 'no persistence, no filtering, no drag-and-drop' are more effective at preventing scope creep than positive instructions alone, because they explicitly close doors the model would otherwise open.