Alright, so everyone's raving about Hailuo's accuracy on their marketing pages and in all the community hype threads. The benchmarks look impressive, until you realize they're using curated datasets that probably don't reflect your specific, messy, real-world tasks. We've been piloting it for technical documentation generation, and I had a sneaking suspicion that the "high accuracy" was situational at best.
I decided to move past eyeballing outputs and actually measure it. The vendor provides an API, but of course, no built-in validation tool that would let you systematically catch hallucinations or factual drift against your own source material. So I built a script. The core idea is simple: feed Hailuo a set of known, correct inputs from our internal knowledge base (the "ground truth"), have it generate the corresponding output (like a summary or a reformatting), and then programmatically compare the AI's output to the known-good version.
The comparison isn't just a simple diff. It uses a combination of semantic similarity (because phrasing can vary) and keyword/entity extraction to flag when critical technical terms are missing, invented, or misrepresented. We're not looking for a perfect match, but for dangerous deviations. I run it against a batch of 100 documents we know are correct.
The initial results were… enlightening. While Hailuo scored well on stylistic tasks, on documents requiring precise adherence to proprietary API endpoint specifications or internal workflow names, the hallucination rate was close to 15%. That's not something you'd catch with a casual review. It lulls you into a false sense of security with beautifully written prose that happens to be wrong on key details.
This approach forces a cost-benefit analysis. If you need to fact-check 15% of a model's output manually, what's the true time cost? You've just added a significant QC overhead, which the per-token pricing doesn't reflect. It also exposes a migration pitfall: if you later switch vendors, you'll need to re-run this entire validation suite, because each model has its own unique failure modes. You're not just swapping an API call; you're potentially inheriting a whole new set of inaccuracies that your previous safeguards weren't tuned to catch.
The script isn't pretty, but it's effective. It's saved us from deploying several batches of confidently wrong documentation. Before you integrate any of these services into a pipeline where accuracy matters, you need a quantitative, automated way to verify their claims against your data, not theirs. Otherwise, you're just trusting their marketing and hoping your use case magically aligns with their test set.
Just my two cents
Skeptic by default
That's a really smart approach. We've been considering Hailuo for customer support snippets, and I was just wondering how you'd even start to test it. Your method of using your own internal docs as the ground truth makes so much sense.
You mentioned combining semantic similarity with keyword checks. How do you handle it when the phrasing is totally different but still technically correct? Does your script give a lot of false positives for that?