/lovable-issues

Preventing Feature Skipping in Lovable Code Generation

Find out why Lovable may miss details without clear prompts and learn best practices to ensure detailed, accurate output.

Matt Graham, CEO of Rapid Developers

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Why Lovable Ignores Details Without Clear Prompt Structuring

Lovable often skips small or strict details when prompts are vague because the chat-first model and its environment prioritize intent, default behaviors, and feasible actions over unstated constraints. If you don't mark a detail as an explicit constraint or give a clear structural signal, Lovable treats it as optional context and resolves ambiguity toward what it thinks is the best overall outcome.

 

Core reasons Lovable drops details

 

  • Ambiguity resolution: Lovable favors a coherent, useful outcome when a prompt leaves trade-offs unspecified, so optional details get deprioritized.
  • Instruction hierarchy: Without explicit structural signals (must/required, file-level edits, precise paths), Lovable interprets details as preferences rather than constraints.
  • Chat-first affordances: Lovable's primary actions are edits, diffs, Preview, Publish and GitHub sync — it can't run local CLIs. That shapes what it assumes is feasible.
  • Context and token limits: Long or mixed instructions can bury specifics. Lovable compresses context and may drop lower-priority items to stay focused.
  • Default heuristics: Models apply general patterns (e.g., prefer simpler code, typical folder layouts). If your detail contradicts those patterns but isn't highlighted, it can be overwritten.
  • No persistent background processes: Each chat turn is evaluated anew. Details not framed as enduring constraints can be lost in subsequent edits.

 

How Lovable’s workflow influences that behavior

 

  • Action set is explicit: Lovable only performs chat-native edits and file operations. It won't assume you want external steps unless you use GitHub sync or say “export to GitHub”.
  • Preference vs requirement: The model distinguishes soft language ("prefer", "if possible") from hard language ("must", "required in src/config.ts"). Soft language often becomes optional.
  • Ambiguous scope: If you mix high-level goals with many low-level constraints in one message, Lovable consolidates toward implementable work and can drop low-salience details.

 

// Lovable prompt — create a short in-repo explainer file
// Paste this into Lovable chat. Ask Lovable to create the file and open Preview.

Create a new file at docs/why-lovable-ignores-details.md with exactly the following content:

# Why Lovable may ignore prompt details

Lovable prioritizes clear, structured constraints. When prompts are ambiguous or mix high-level goals with many low-level preferences, Lovable resolves toward coherent, implementable outcomes and may treat unclear details as optional.

Key causes:
- Ambiguity resolution: vague trade-offs are deprioritized.
- Instruction hierarchy: unspecified items become preferences.
- Chat-first limits: only chat-native edits, no terminal steps.
- Token/context limits: low-salience details can be dropped.

Do not change other files. After creating the file, open Preview and show the diff for docs/why-lovable-ignores-details.md.

 

// Lovable prompt — append a short note to README
// Paste this into Lovable chat. Ask Lovable to modify README.md.

Open README.md at the repository root and append the following short note at the end of the file:

## Note about prompt details

See docs/why-lovable-ignores-details.md for a short explanation of why Lovable sometimes treats unstated details as optional. This helps contributors write clearer prompts and repo-level guidance.

Save changes and show the diff in Preview.

 

Still stuck?
Copy this prompt into ChatGPT and get a clear, personalized explanation.

This prompt helps an AI assistant understand your setup and guide you through the fix step by step, without assuming technical knowledge.

AI AI Prompt

How to Ensure Lovable Honors Prompt Details

 

Direct answer

 

Make Lovable honor prompt details by adding a small, repo-local prompt validator + auto-augmenter, patching each place that sends prompts to the LLM to validate/augment before send, documenting canonical structured prompts, and using Lovable's Secrets UI + Preview to test — all applied with Chat Mode edits and file patches in Lovable (no terminal required).

 

Step-by-step Lovable prompts to paste (exact, paste one at a time)

 

  • Prompt A — Find where your app sends prompts to the LLM
    Paste this into Lovable chat to let it locate targets to edit:
Search the repository for all files that create or send prompts to an LLM. Look for usages of strings or arrays named "prompt", "messages", "system", functions calling "fetch(" with "openai" or "chat", or library calls like "createChatCompletion", "chat.completions", "client.chat", or "openai.chat". Return a list of file paths and a 8-line code excerpt around each match. Provide the list so I can tell you which files to modify.

 

  • Prompt B — Add a repo-local prompt validator + augmenter
    After you get the list from Prompt A, paste this to create the validator file. It must be created exactly at src/utils/promptValidator.js and used by the next patch step.
Create file src/utils/promptValidator.js with the following content. This file must export validatePromptObject(prompt) and augmentPromptForMissingDetails(promptObj). Use plain JS (no new deps).

```js
// src/utils/promptValidator.js
// Basic structure validator and augmenter — no external deps
function validatePromptObject(p) {
  const errors = [];
  if (!p || typeof p !== 'object') errors.push('prompt must be an object');
  if (p && !p.task) errors.push('missing "task" (string)');
  if (p && p.required_fields && !Array.isArray(p.required_fields)) errors.push('"required_fields" must be an array of strings');
  return { valid: errors.length === 0, errors };
}

function augmentPromptForMissingDetails(p) {
  // Ensure we always produce a messages array to send to an LLM
  const messages = [];
  // System instruction to always honor "required_fields"
  const fields = Array.isArray(p.required_fields) ? p.required_fields : [];
  const system = { role: 'system', content: 'Always follow the user instructions and explicitly include the required details listed.' };
  messages.push(system);
  // If there are required fields, append a clear requirement
  if (fields.length) {
    messages.push({ role: 'system', content: 'Required details: ' + fields.join(', ') + '. If any are missing in the user text, include them in the response and call out which you added.' });
  }
  // Include context or examples if provided
  if (p.context) messages.push({ role: 'system', content: 'Context: ' + p.context });
  // User message: the main task text
  messages.push({ role: 'user', content: p.task });
  // Optionally enforce response_format
  if (p.response_format) {
    messages.push({ role: 'system', content: 'Required response format: ' + p.response_format });
  }
  return messages;
}

module.exports = { validatePromptObject, augmentPromptForMissingDetails };

 

<ul>

  <li><b>Prompt C — Patch each prompt-sender file to validate + augment before sending</b><br/>
  After you provide the file list from Prompt A, paste this and replace <b>[FILE_PATH]</b> with each path from the list (paste one patch per file). Use Lovable's edit/patch feature to modify the file in-place.</li>

</ul>

```text
Edit the file at [FILE_PATH]. At the top add an import:
const { validatePromptObject, augmentPromptForMissingDetails } = require('./src/utils/promptValidator.js');

Find the code that currently builds the prompt or messages and calls the LLM. Replace the send logic with this pattern:

```js
// validate and normalize prompt input
const inputPrompt = /* the existing prompt object or string variable */;
let promptObj = inputPrompt;

// If project currently passes raw strings, wrap them into an object
if (typeof inputPrompt === 'string') {
  promptObj = { task: inputPrompt };
}

const { valid, errors } = validatePromptObject(promptObj);
if (!valid) {
  // respond with a clear error so front-end / Preview shows what's missing
  return res?.status?.(400)?.json ? res.status(400).json({ error: 'Invalid prompt', errors }) : Promise.reject({ error: 'Invalid prompt', errors });
}

// produce messages array augmented to ensure required fields are honored
const messages = augmentPromptForMissingDetails(promptObj);

// replace the original call that sent messages/prompt to the LLM to send 'messages' instead
// example for fetch/openai usage — adapt the existing send-call shape
const response = await sendToLLM({ messages });

Make only the minimal surrounding change to wire validate+augment; preserve existing API keys, response handling and error flows.


&nbsp;

<ul>

  <li><b>Prompt D — Add developer docs and example structured prompts</b><br/>
  Create docs/prompt-guidelines.md describing the required prompt shape and give copy-ready examples. Put it at docs/prompt-guidelines.md so teammates and QA can paste structured prompts into the app or Lovable.</li>

</ul>

```text
Create file docs/prompt-guidelines.md with examples of structured prompts and one recommended "copy-paste" user prompt. Include a short checklist for QA: set OPENAI_API_KEY in Secrets UI, run Preview, send these example prompts, confirm responses explicitly include each required field.

 

  • Prompt E — Secrets + Preview checklist (manual steps for the developer)
    Paste this into Lovable chat to show a checklist and to remind to set secrets in Lovable Cloud Secrets UI before Preview/Publish.
Create a checklist note in the repo (docs/lovable-checklist.md) that reminds to:
- Open Lovable's Secrets UI and add OPENAI_API_KEY (exact key name: OPENAI_API_KEY).
- Use Preview to run the example prompts from docs/prompt-guidelines.md and verify outputs include required_fields.
- After successful Preview tests, Publish changes.

Write the checklist with step-by-step instructions so a non-technical reviewer can follow Secrets UI -> Preview -> Publish.

 

Why these steps work in Lovable

 

  • Validator + augmenter runs inside the app so Lovable’s Preview and Publish will behave the same as production — no local CLI needed.
  • Use Chat Mode edits and file patches (the prompts above) to make concrete file changes inside Lovable, then use Preview to test and Secrets UI for API keys.
  • Documented structured prompts make it less likely humans paste ambiguous free-form prompts that get ignored by the model.

 

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Best Practices for Getting Detailed Output from Lovable

Best practice: ask Lovable for complete, explicit outputs (full-file contents + unified diffs + tests + commit messages), request line-by-line comments and a short reproduction checklist, and use Preview/GitHub sync/Secrets UI for anything that touches secrets or needs a terminal — always tell Lovable exact file paths, branch names, and whether you want a patch or full-file replacement.

 

Quick actionable prompts to paste into Lovable

 

  • Full-file + unified diff + explanation: "Update src/App.tsx: replace the entire file with a complete implementation that adds the block for /dashboard and /login. Provide the full file contents and also output a unified diff (git-style patch) showing only the changes. Add inline comments above each exported function explaining why. End with a 3-line commit message."
  • Add unit tests and a reproduction checklist: "Create tests for src/api/user.ts. Add file tests/api/user.test.ts using Jest. Include mock data and at least three test cases (success, validation error, network error). Provide a short 'How to reproduce locally' checklist describing required env vars and exactly which Lovable Secrets to create (name and purpose)."
  • Secrets setup note (Lovable Secrets UI): "When tests require credentials, list each secret name and where to set it in Lovable Secrets UI (e.g., API_URL, SUPABASE_KEY). Do not include secret values — only variable names and examples of formats."
  • If output is truncated: "If you stop mid-file, continue from the last line shown. Use this exact follow-up if truncated: 'continue output from the last line of [file path] and then provide remaining files as full contents'."
  • Branch/PR-ready patch with description: "Create a branch named fix/user-api-logging. Produce a single patch that updates src/api/user.ts and adds tests/api/user.test.ts. Include a PR title and a detailed PR body that lists changed files, motivation, testing steps, and a short changelog. Show the full files and the git-style unified diff. Note: I will use GitHub export/sync to push the branch (outside Lovable if needed)."
  • Ask for step-by-step reasoning + code comments: "For every code change, add a 'Design note:' comment block above the function describing trade-offs, expected runtime, and why alternative X was rejected. Then summarize changes in 4 bullets at the top of the patch."

 

Practical tips to get the most detail

 

  • Always request both full-file contents and a git-style patch — patches are easier to review and full files avoid ambiguity.
  • Ask for tests, mocks, and a minimal reproduction checklist so outputs are actionable without guessing env setup.
  • Use explicit file paths, branch names, and single-responsibility prompts (one change per prompt) to avoid partial or mixed outputs.
  • Use Preview to inspect changes before Publish, and use GitHub export/sync when you need terminal-level control (label that step as outside Lovable).

 

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