Turn websites into ChatGPT context

Why websites needs preparation before ChatGPT

Documentation and reference pages are often the missing context behind a codebase, client project, or research question.

ChatGPT is useful for general analysis, writing, coding help, and iterative work when the context is packaged into a readable file. The result depends heavily on how cleanly your source material is grouped, labeled, and filtered before it reaches the model.

Step-by-step workflow

Add the public URL, let Riflet extract readable page text, then combine it with local files or repos.

• Add website pages as a Riflet source.
• Select documentation pages, product pages, help articles, changelogs, public specs, and reference pages.
• Use filters to remove stale exports, generated files, and unrelated background material.
• Export one Markdown or text bundle and use it in ChatGPT Projects.

What to include

For this workflow, prioritize documentation pages, product pages, help articles, changelogs, public specs, and reference pages. Add a short instruction file at the top if the AI needs naming conventions, project goals, or constraints.

Scraped pages are snapshots. Re-export when the website changes or when you need a different section of the site.

Getting the bundle into ChatGPT Projects

ChatGPT Projects keeps uploaded material in project files. Create a project and attach the bundle to it, so every chat started inside that project can read the file without you pasting anything. It accepts text, Markdown, PDF, and Office documents, so a Markdown or plain-text export from Riflet needs no further conversion.

ChatGPT retrieves passages from attached files rather than reading each one end to end. Explicit file-path headers and section markers matter more here than anywhere else: a bundle with clear boundaries gets retrieved reliably, an undifferentiated wall of text does not.

Project files persist until you remove them, so a stale bundle keeps answering with outdated facts long after the sources moved. Replace it on the same schedule as the work.

Token budget advice

Riflet lets you check the bundle against GPT-6 Astra's 1,050,000-token context window before export. Keep room for your actual question and for the model's answer.

If the bundle is too large, remove files that merely repeat what another file already explains. Context quality usually beats context volume.

Honest limitation

Riflet does not push live updates into ChatGPT. It creates the context file. When the source changes, re-export and upload the new bundle yourself.