Turn websites into local LLMs context

Why websites needs preparation before local LLMs

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

Local models are useful for private workflows, but they usually need tighter bundles because context limits vary by runtime. 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 local LLMs.

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 local LLMs

local LLMs keeps uploaded material in the prompt itself. Paste the bundle into the prompt, or load it through the front-end you run, such as LM Studio, Ollama, or a text-generation web UI. It accepts plain text and Markdown, so a Markdown or plain-text export from Riflet needs no further conversion.

The usable context limit is often far below the number on the model card, because quantisation and available VRAM cap what you can actually load. Check the limit your runtime reports, not the headline figure, before sizing the bundle.

Nothing persists between runs, so the bundle is re-sent every session. That makes bundle size a recurring cost rather than a one-time one, and it is the strongest argument for trimming hard.

Token budget advice

Riflet lets you check the bundle against Local LLM's 32,768-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 local LLMs. It creates the context file. When the source changes, re-export and upload the new bundle yourself.