We found a bad pattern in Research AI: some UI blocks looked factual but were not backed by original sources.

The obvious example was Hot discussions. It showed platform names, handles, engagement counts, and quotes. They looked like real posts. They were not. They were OpenAI-generated representative snippets.

That is not acceptable for a research product.

The rule

LLMs can synthesize. They cannot be the source of truth.

So we changed the pipeline:

Worker fetches real source URLs
  → OpenAI summarizes only from those collected sources
  → worker validates output URLs
  → worker writes SQLite cache
  → FastAPI reads cache
  → Next.js renders cached fields

FastAPI does not call OpenAI. Next.js does not generate fallback answers. If the cache misses, the API queues work and waits for the worker.

What we removed

We removed paths that could quietly produce fake-looking data:

• Frontend serverless OpenAI generation.
• Demo research payloads.
• Static trending topic lists.
• Local topic warmers in Next.js.
• AI-generated “discussion quotes,” fake handles, and fake upvotes.
• Source prompts that allowed “use a Google search URL if unsure.”

That last one matters. A Google search URL is not a source. It is a way to look for a source.

The new source boundary

Before OpenAI writes a verdict, the worker collects candidate sources from real endpoints:

• Hacker News Algolia API
• arXiv API
• Wikipedia API

The worker passes those candidates into the OpenAI prompt as allowed_sources.

Then it checks the answer after generation. Every sources[].url must match one of the URLs collected by the worker. If OpenAI invents a link, the worker drops it. If no verified source remains, the worker fails the job instead of writing a fake result.

Hot discussions changed too

We stopped pretending we had original posts.

The UI now shows verification entry points:

• X live search
• Hacker News search
• Reddit search

No invented author. No invented quote. No invented engagement count.

That is less flashy. It is also honest.

Cache freshness

Research content gets stale. The FastAPI read path now checks cache age. If a result is too old, it does not serve the stale payload as if it were fresh.

Instead:

1. FastAPI marks the result stale.
2. FastAPI queues a force_refresh job.
3. Worker fetches fresh source URLs.
4. Worker calls OpenAI with those sources.
5. Worker overwrites the cache.

Again: FastAPI reads and queues. Worker computes.

The checklist we use now

Before shipping any AI-generated content block:

• Does this claim have a URL?
• Did the URL come from code fetching a real source, not from the model?
• Does the model have permission to cite only those URLs?
• Do we reject or drop citations outside the allowed set?
• Does stale cache trigger worker refresh?
• Does the UI make search links look like search links, not original quotes?
• Is there any demo fallback that can reach production?

If the answer is weak, the feature is not ready.

The bigger lesson

Fake data usually does not start as fraud. It starts as a shortcut: “just make the UI feel complete.”

That shortcut is dangerous in AI products. A polished fake is worse than an empty state because users may trust it.

For Research AI, the right boundary is simple:

source first, synthesis second, cache third, render last.