Giant Context does a great deal of work no one is waiting on. Pages that take minutes to build, sites to crawl and import, its own recurring maintenan...
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Apr 24, 2026Giant Context builds your marketing site with a model, and it writes without being asked. That makes where the content comes from the whole question.
A model handed nothing but a request invents. It writes a warm testimonial no customer gave, a user count from nowhere, a feature the product does not have. That leaves you with a site full of claims that were never true.
You bring your own material, your documents and specs and pricing and the record of the business. The site is built from that, and every claim on the page traces back to something you provided.
A model told to build a homepage with nothing behind it has a testimonial section to fill and nothing to fill it with, so it fills the section with something plausible. It is not lying. It is completing a pattern, and an empty testimonial section is a pattern with an obvious completion.
Left alone it produces a site that looks finished. The failure is quiet, because an invented fact looks exactly like a real one.
The fix is to give the model something real to build from, and that material is your own files. Your business has a corpus of them, product docs, pricing sheets, case studies, the team page, the specs, the record of what it does and who it is. Once you upload that corpus it becomes the ground the site is built on. Before the model plans a section, the grounding pipeline reads the files and does two things with them. Its own description states both:
The grounding pipeline, in its own words
Grounding. Retrieval conditioning + verification against knowledge_chunks.
1. Retrieval conditioning. Fetch the chunks most relevant to a query and format them as a PROJECT FACTS block ... so generation anchors on real project data instead of inventing values.2. Verification. For each extracted inventory item, search for a fact-labeled chunk above a cosine similarity threshold, and drop items that don't anchor. Stops hallucinated testimonials / stats / team-members at the source.The first conditions the writing. It pulls the file content most relevant to the section at hand and puts it in front of the model as real project data, so the model writes from your material rather than from nowhere. The second verifies. Every concrete fact the model proposes, a testimonial, a statistic, a named team member, has to anchor to file content above a similarity threshold. Anything that does not is dropped. A claim reaches the page only if it came from your files.
A law firm uploads three things, a page of practice areas, two closed-case write-ups and a rate sheet. The site that comes back lists those practice areas, quotes the two cases as proof of work, and shows the rates from the sheet. It invents no fourth practice area to balance the grid and manufactures no client quote to fill the testimonial row, because neither is anywhere in the files. The page is shorter than a fabricated one would be.
Not everything in your files is a fact to state. You upload your own pricing. You also upload a comparison against a competitor. The first is yours to claim. The second is background, useful for reading the market and not a price you charge.
So every file is tagged when you upload it, fact or context. The verification pass will not let a context chunk anchor a claim, so the competitor's prices never surface as yours. You set the tag at the moment you hand over the file. The model never guesses which prices are yours.
A fact file | A context file | |
|---|---|---|
What it holds | The customer's own facts, pricing, cases, team | Background, like a competitor comparison |
Can anchor a claim | Yes, its content can become a stated fact | No, never a stated fact |
Its job on the page | Provides the claims | Shapes the emphasis and framing |
The result is a site you can stand behind. Every line of it came from something you handed over.
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