---
slug: draftlytic-for-developers
title: "How a Working Developer Should Use Draftlytic"
excerpt: "Draftlytic for developers isn't about hand-holding. It's about using the planning gap most engineers skip, then handing the result to Cursor or Claude Code."
primaryKeyword: "Draftlytic for developers"
publishedAt: 2026-04-05
readingTimeMin: 7
author: "Robert Boylan"
tags:
  - draftlytic
  - developer
  - app-planning
  - cursor
  - claude-code
  - implementation-plan
---

You've been writing code professionally for ten years. You can read a Postgres explain plan, you've shipped distributed systems, you debug from stack traces and prefer your terminal to your IDE on hard problems. The idea that you'd reach for a "guided questionnaire to plan an app" probably feels a step below your skill grade.

It does, until the third evening in a row where Cursor has built three slightly different versions of the same dashboard component, and you realise the issue isn't the model. The issue is that nobody (including you) wrote down what the dashboard actually was. The "I can just code it" instinct, on side projects especially, is what keeps engineers in the loop of building, scrapping, rebuilding.

Here's how Draftlytic for developers actually fits, without pretending you're the founder persona.

## The instinct, and where it fails

The "I can just code it" reflex makes sense at work. You have a Jira ticket. Someone else wrote the spec. You're paid to translate spec into code. Skipping the planning step isn't really skipping it; it's relying on the org's planning to have already happened.

On a side project, there is no org. Nobody wrote the ticket. You're the spec author and the implementer, and the spec is the thing you keep skipping because it feels like overhead when you could just open Cursor and start typing.

The cost shows up in the third evening, not the first. The first evening you've shipped something. By the third, you've made three structural decisions you don't remember making, three half-features that don't fit together, and an AI tool that keeps suggesting fixes for the symptoms of an underlying shape problem. The most senior engineer I know has admitted to spending more weekends like this than weekends shipping.

The skip happens because writing a spec for yourself feels redundant. You know what you want. The trick is that "what you want" is a feeling, not a document, and the AI tool can't read feelings. It can only read what you give it.

## What changes in the question flow for developers

If you sign up to Draftlytic and select Developer as your user type, the question flow shifts. There's a longer post on [how the user-type question reshapes the rest of the flow](/blog/developer-vibe-coder-or-founder), but the short version: developer mode skips the explainer questions and front-loads the technical decisions.

You won't be asked what an API is. You will be asked which API patterns the app uses (REST, RPC, event-driven, hybrid). You won't be asked what a database is. You will be asked which entities are core, which are peripheral, and what the foreign-key relationships look like. You won't be asked what auth is. You will be asked whether you want session-based or token-based auth, whether you need multi-tenancy, and whether SSO is in scope.

The questions that were going to take a non-tech founder fifteen minutes are not the questions you're answering. The flow respects your experience and gets to the decisions you'd otherwise be making in your head while typing into Cursor.

## The fields the AI uses verbatim

A few sections of the export are the ones that change AI output the most for technically-fluent users.

The **tech stack** field is read literally by every downstream prompt. If the spec says "Postgres on Supabase, Next.js 15 with the App Router, Tailwind 4, deployed to Vercel," that's exactly what shows up in the generated code. No more "actually here's a Firebase example" when you didn't ask for Firebase.

The **data model** is also read literally. Field names you put in the spec are the field names that appear in the generated migrations and TypeScript types. This sounds small until you've spent forty minutes renaming `userId` to `user_id` across thirty files because Cursor used the wrong convention for your codebase.

The **external services** list is what stops the AI inventing fake third-party integrations. If you've written "Resend for transactional email", the generated code uses the Resend SDK. If you leave that field blank, the AI will sometimes invent an email module from scratch, complete with imaginary API endpoints, and you find out at runtime.

The **architectural constraints** section (free-text in developer mode) is where you write down the non-obvious rules: "all database access goes through the repository layer in `lib/db/`", "no client-side direct Supabase calls in pages, only through server actions", "shared types live in `lib/types/`, not co-located with components". These are the kinds of rules a senior engineer would enforce in code review and a fresh AI session has no way to know.

## Why the implementation plan is most useful for developers

Draftlytic exports two documents from a complete spec: the PRD itself, and an implementation plan. There's a longer write-up on [what's in the implementation plan and how it works](/blog/export-implementation-plan), but for a developer audience the implementation plan is often the more valuable of the two.

A PRD answers "what is this?" An implementation plan answers "what's the build order, in phases, with each phase shippable?" That's the kind of decomposition senior engineers do mentally before they touch the keyboard. Writing it down has two effects: it lets the AI follow the same order you'd have followed, and it lets you (or a teammate, or future-you) pick up the build mid-stream without having to re-derive the structure.

The implementation plan also catches a category of mistake that AI tools make when they're given a feature list with no order: they build the visible features and skip the foundational ones. The auth flow, the data layer, the deploy pipeline, the error tracking. These tend to land late or never when the AI is reading a feature-priority list. The implementation plan, by contrast, puts the unsexy infrastructure first because it has to.

## The workflow: spec in Draftlytic, code in Cursor or Claude Code

In practice, a senior dev's Draftlytic session looks like this. Twenty to thirty minutes filling out the questionnaire (faster on subsequent projects once you know the rhythm). Generate the spec. Read it once, edit anything that doesn't match what you actually meant. Export both documents.

Open Cursor. Paste the PRD into the chat. Start with the first phase of the implementation plan. Build, review, ship that phase. Open the next session, paste the same PRD plus a note about what's already done, build the next phase.

For larger structural changes, drop into Claude Code. The PRD plus implementation plan gives Claude Code the kind of context that lets agentic loops actually work, which is what [makes Claude Code a serious option for cross-codebase changes](/blog/cursor-vs-claude-code).

The spec stays the through-line across sessions. When you come back to the project after a month, you don't reconstruct from memory. You paste the spec back in.

## What this isn't

This isn't an argument for adopting heavyweight process. The whole pipeline (questionnaire, spec, implementation plan, AI-driven build) is shorter than a single sprint planning meeting at most companies. It's also entirely optional once you've done it a few times. Plenty of engineers internalise the questions after two or three projects and do the planning faster in their head than the questionnaire would.

The case for using the tool isn't that you can't do this yourself. It's that the cost of doing it yourself, every time, on every side project, is exactly the friction that keeps so many engineering side projects half-built. The questionnaire is faster than the equivalent thinking-from-scratch by enough margin that even seasoned engineers tend to use it on projects they thought they didn't need it for.

Draftlytic isn't pitching itself as smarter than you. It's pitching itself as the structured forty minutes you'd otherwise turn into a vague hour at the keyboard. There's also a Pro tier with [an unlimited project allowance and the implementation plan export](/pricing) when one project is no longer enough.

The skill ceiling for AI-assisted development isn't writing better prompts. It's writing better specs. That's the layer most engineers haven't built a habit around yet, and it's the layer where the next year of productivity gains will come from.