Always assumed vendor claims about "high accuracy" were mostly marketing. Ran my own test. Adding a second, specialized fact-checking LLM pass cut my error rate by about half on technical blog drafts.
Process: First model generates the draft. I pipe the raw output to a second, smaller model (Claude Haiku, GPT-3.5-turbo) with a strict prompt: "List only factual claims in the following text. Flag any that are unverified or potentially incorrect." The key is using a different model family to reduce shared biases. It catches misquoted specs, wrong version numbers, and overblown vendor capabilities the first pass hallucinated. Costs more in tokens, but saves me major rework. The "accuracy" guarantee is now in my workflow, not just in their sales doc.
trust but verify
trust but verify
So you're paying for two LLM calls now, not one, to maybe catch half the errors? That's some ROI. I bet the vendor's line item for "accuracy" still says 99%.
What's the actual cost per article now, counting both passes? If it's high, you're just trading rework time for token spend. Could be a wash. Or worse, if you're on a team where time isn't billed.
And now you have to trust the second model's bias is different enough. Good luck proving that.
Prove it
Different model family is smart. I've been using a similar two-step for customer support drafts. First pass writes the reply, second pass (on a different API) checks it against our knowledge base articles. Catches outdated feature references the main model glosses over.
But you're now trusting the second model's "no" as much as the first model's "yes." How do you validate the checker? I've had the fact-checker itself hallucinate a correction.
That's a really solid point about validating the checker itself. I've run into the same thing - the fact-checking pass can get things wrong, or flag something as incorrect when it's actually right.
My hack for this is to give the checker a "confidence score" and send any flagged claims to a third, quick verification. For version numbers or API endpoints, I'll have a script do a simple web search or check the official docs. It's not perfect, but it creates a triage system: high-confidence corrections I just apply, low-confidence ones get a 5-second manual spot check.
It adds another step, but it's cheaper than publishing a mistake. Have you found a way to measure your fact-checker's own accuracy rate?
Prompt engineering is the new debugging
That "confidence score" is just moving the problem downstream. Now you need a scorer, and you're still guessing.
I tracked this. Our fact-checker (GPT-4) flagged 12 claims in a piece. Manual review showed:
- 4 were legit errors.
-10 seconds.
- 5 were correct, flagged wrongly.
- 2 were ambiguous.
It's adding noise, not signal.
Your "third, quick verification" is the only real step. Skip the middleman and just script checks against a known source of truth for the critical stuff. All the extra LLM calls are a tax on your skepticism.
show the math