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Ask Lovable to analyze data, create documents, transform files, and generate downloadable outputs directly in the project chat. Lovable writes and runs scripts in an isolated environment, then returns the result as a generated file. Your project’s source code is never affected.
To create Markdown, text, CSV, or JSON files without a project, use Chats.
You can build on top of the result in the same conversation. After Lovable analyzes your data or generates a file, ask it to create app features based on what it found, for example by turning a spreadsheet into a dashboard or a product spec into a working app flow.
On projects using the built-in backend (Cloud), code execution has access to your database only if the Read database and Change data permissions are set to Always allow. See Cloud for more information.

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What you can do

Upload a CSV, spreadsheet, or any supported file and ask Lovable to analyze, transform, or combine it with other data sources, such as your database or connected tools.

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Presentations and slide decks

Lovable creates presentations in two ways:
  • As a web app: Ask for a presentation and Lovable builds the deck as a web app in your project. Slides present fullscreen in the browser, you refine individual slides in the project chat, and you can publish the deck and share its link like any other project.
  • As a downloadable file: Ask for a PDF or .pptx file and Lovable generates it like any other file on this page. Download it from the project chat or the Files tab.

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How it works

Describe what you need in the project chat. You do not need to configure commands, write scripts, or choose tools yourself. Lovable decides when to run code and when to build features based on your request. Lovable typically follows this flow:
  1. It reads the files, data, or context you provide.
  2. It writes and runs the scripts needed to complete the task.
  3. It checks the result and fixes issues before delivering, for example layout problems in PDFs or missing data in generated files.
  4. It returns the result as inline analysis, a generated file, or both.
  5. It keeps that context in the same conversation so you can refine the output or build on it.
The more specific you are about structure, formatting, tone, data sources, and the result you want, the better the output will be.

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What Lovable can run

Lovable writes and executes scripts in an isolated Linux environment. If something can run in a Linux shell, Lovable can likely run it. Common tools are preinstalled, for example: If a task needs an additional package or command line interface (CLI) tool, Lovable can install it automatically.

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Supported file types

Lovable can read files you upload and generate new files in the following formats: Files you upload for analysis can be up to 20 MB each on the Free plan, 256 MB each on paid plans, and 1 GB on Enterprise, with up to 10 files per message. Generated files are stored in your project’s Files area, not in your project’s code, so they are not subject to these upload limits or to the 10 MB per-file code limit.

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Manage generated files

When Lovable creates a file in a project, it appears in the project chat as a file card. You can open it directly from the project chat, or open the Files tab to search, preview, and download all files created in your project, or reference a file directly in the project chat. You can refine generated files through follow-up prompts. When a file is revised, Lovable creates and stores a new version (for example, report_v2.pdf) so you can compare them. Files are securely stored with the project until you delete them. In the Files tab’s grid view, images display at their real proportions. New files appear in the tab as Lovable creates them. To find a file, use the search box, filter by type with All, Images, Videos, and Documents, sort by Updated, Name, or Size, or switch between Grid view and List view. From a file’s actions menu, you can:
  • Add to chat: reference the file in your next request.
  • Add to collections: add the file to one or more collections.
  • Download: save the file to your device.
  • Copy link: copy a link to the file.
  • Copy to clipboard: copy an image so you can paste it elsewhere. Available for images only.
  • Delete: remove the file from the project. You can also ask Lovable to delete a file. Deleting a file cannot be undone.

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Upload your own files

You can add your own files to a project alongside the ones Lovable generates, for example a brand guide, a dataset, or images for Lovable to use. Select New → Upload new files, or drag files onto the Files tab. You need edit access to the project to upload files. Uploaded files are stored in the project’s Files tab, not in its code. Folders are not supported, so choose individual files. Each file can be up to 20 MB on the Free plan, 256 MB on paid plans, and 1 GB on Enterprise, the same limits as for files you attach in the project chat.

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Collections

A collection is a named group of files in a project, available on all plans. Use collections to keep related files together, for example the assets for one campaign or the exports for one report. A file you add to a collection stays in the Files tab and can belong to several collections at once.
  • Create a collection: select New → Create new collection, or select Create collection under Collections.
  • Add files to a collection: open the collection and select Add files, or open a file’s actions menu and select Add to collections.
  • Rename or delete a collection: use the collection’s actions menu. Deleting a collection does not delete its files.
  • Remove a file from a collection: open the collection, then open the file’s actions menu and select Remove from collection. The file stays in the Files tab.
Each project can have up to 100 collections, and each collection up to 500 files. You need edit access to the project to change collections. You can also ask Lovable to organize your files into collections. Collections appear in the project chat as cards you can open. Select Add to chat on a collection to reference it in your next request.

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Tips for best results

  • Be specific about formatting
    ”Create a PDF report with a cover page, section headers, and a summary table” produces better results than “Make a report”.
  • Name your output
    If you want a specific filename, ask Lovable to add it, for example “Save it as q1-summary.pdf.”
  • Iterate
    If the first result is not perfect, describe what to change. Lovable keeps context from prior outputs.
  • Chunk large datasets
    For very large files, ask Lovable to process a subset first, then scale up.
  • Use connected tools as data sources
    Ask Lovable to pull data from tools like Slack, Linear, or Notion, for example “Summarize the last 50 messages from my Slack feedback channel and generate a report”.
  • Combine steps
    You can ask for analysis and file generation in one prompt, for example “Analyze this CSV and export the top 10 rows as an Excel file.”