{
“title”: “TruthBot Upgrade: Process Over Prompts”,
“Text1”: “
Telling a language model to ‘fact check this’ is basically a coin flip. TruthBot just shipped a major upgrade that replaces the coin with a structured process.
What dropped
u/Smooth_Sailing102 released a new version of TruthBot, a CustomGPT for claim verification and persuasion analysis. The upgrade brings improved claim extraction, stronger rhetorical analysis, and a brand new synopsis engine. Fully free, no data collected, and the entire logic tree is public in a Google Doc with eight tabs.
The claim extraction improvement is significant on its own. Earlier versions would sometimes catch the overall theme but miss specific embedded claims, especially ones buried mid-paragraph or framed as casual background context. The new version isolates individual assertions regardless of where they sit in the text. The rhetorical analysis upgrade adds detection of framing patterns that often fly under the radar: things like appeal to novelty, false urgency, and manufactured consensus. And the synopsis engine means you get structured output at the end instead of a wall of analysis you have to interpret yourself.
The twist
Most people assume a better prompt fixes hallucinations. It doesn’t.
LLMs generate probable-sounding text, not verified facts. A model can produce fluent, specific, confident output even when the underlying claim is outdated, missing, or entirely invented. And once a hallucination lands in a response, it spreads into summaries, into follow-up drafts, into new conclusions built on bad premises. It compounds.
Think about what that looks like in practice. You ask an AI to summarize a research topic. It includes a statistic that sounds authoritative. You use that statistic in a report. A colleague reads the report and cites the number in their own presentation. Three weeks later someone traces it back and finds it doesn’t exist in any actual source. That chain started with one confident-sounding output that nobody stopped to verify. The problem isn’t the model being careless. It’s a process failure, the same kind that plagues any information pipeline without built-in review layers.
The fix isn’t a smarter prompt. It’s a structured process that forces isolation, source-checking, and explicit uncertainty labeling before anything gets treated as fact. The same way a solid editorial process at a publication doesn’t rely on writers having perfect recall, it builds in review steps that catch what any individual misses.
How TruthBot runs it ⚙️
- 🔍 Claim extraction: isolates specific statements, not just the general topic. It treats “AI is transforming healthcare” and “AI reduced diagnosis time by 40% in a 2023 trial” as two completely different types of claims that need different levels of scrutiny.
- Source independence check: flags multiple sources that trace back to a single origin. This matters more than most people realize. Seeing a claim repeated across five articles feels like broad consensus until you notice all five cite the same original piece, and that piece cites a press release.
- Rhetorical analysis: surfaces persuasion patterns, loaded framing, emotional pressure. Useful not just for spotting manipulation in content you consume but for reviewing your own AI-generated outputs before they go anywhere.
- Contradiction testing: finds where claims conflict with each other or with known evidence. A single document can contain internally contradictory statements without the author or the AI catching it. This step surfaces those.
- Uncertainty labeling: marks what’s verified, what’s plausible, and what’s genuinely unknown. There’s a real difference between “this is false” and “this cannot be confirmed from available sources.” Most tools skip this distinction entirely.
- 📋 Synopsis engine: converts the full analysis into structured, readable output. Instead of raw analysis notes, you get something you can actually act on or hand off to someone else without a translation layer.
Pro tip
The Google Doc with the logic tree is the more valuable asset here. Eight tabs of structured reasoning you can adapt into your own prompts, agent workflows, or custom GPTs. Think of it less as a free tool and more as an open-source blueprint for building disciplined AI verification into anything you work on.
Specifically, the tabs covering source independence and rhetorical pattern detection are worth lifting directly into any content review workflow. If you are running an AI-assisted research or writing process, dropping those logic steps into your existing prompt chain adds a layer of scrutiny that catches the failure modes LLMs are most prone to. You don’t need to rebuild anything from scratch. Map the tabs to your current steps and wire them in. Most people find the source independence check alone catches things they would have let through.
Try it 🚀
TruthBot is live as a CustomGPT, with a web app version in the works. The full logic doc is public and open to use or adapt however you want. Both links are in the original thread on r/PromptEngineering. If you work with AI-generated content where accuracy actually matters, adding a structured verification pass changes what you catch.
Start with one document. Something you already produced with AI assistance. Run it through TruthBot and see what the contradiction testing and uncertainty labeling surfaces. Most people find at least one claim they wouldn’t have flagged on their own. That’s the value. Not replacing your judgment, just giving it a structured surface to work against.
”
}
I Built TruthBot, an Open System for Claim Verification and Persuasion Analysis
by u/Smooth_Sailing102 in PromptEngineering