Okay, I have to share this because it was a real rollercoaster. I've been testing Perplexity for research tasks, and I thought, "Let's see how it handles a complex, specific comparison between our software and competitors." So I fed it our detailed, internal product spec sheet—features, integrations, pricing tiers, the whole thing—and asked it to generate a comparison table with three other major tools in our space.
The results were... confidently wrong. 😅
It got basic facts completely backwards. For example:
* It listed one competitor as having **native time tracking**, which it famously does not (it's always via plugins).
* It said our platform **lacks granular user permissions**, which is actually one of our headline features.
* It completely **invented a pricing tier** for another tool that doesn't exist.
What's fascinating is that the *structure* of the output was perfect—a clean, persuasive table that *looked* authoritative. But the content was a mix of outdated info and pure hallucination.
This was a great reminder for my team about AI-assisted research. We can't skip the fact-checking step, especially for nuanced product details. It's fantastic for brainstorming or getting a first draft, but for anything that needs accuracy, it's more of a starting point than a final source.
Has anyone else run into this with Perplexity when using very specific, technical documents? I'm curious if feeding it more context actually increases the risk of it "filling in the blanks" incorrectly.
🌻 fiona
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Oof, that's a classic and frustrating result. The "confidently wrong" part is the real kicker, isn't it? The authoritative formatting makes the errors so much more persuasive at a glance.
It's a perfect example of why these tools need a very specific, constrained role in research. They're great for generating a first-draft structure or finding initial sources, but treating them as a source of truth for specific, niche data points is exactly where they stumble.
Your final point about not skipping fact-checking is the key takeaway for everyone. It's less of a research assistant and more of a very fast, sometimes creative, brainstorming partner that requires supervision.
Stay curious, stay skeptical.
You've hit on the exact reason I don't feed proprietary data into these general models. The polished output format is a trap, giving false credibility to flawed data.
For competitive analysis, I stick to a hybrid approach. Use the LLM to generate a framework or a list of standard evaluation criteria, but then populate that framework manually with data you pull directly from vendor datasheets, recent trial accounts, or your procurement team's notes. Treating it as a data entry clerk instead of an analyst is the only safe bet.
Your example about the invented pricing tier is the most dangerous one. Someone in a hurry could easily base a budget on that.
Your mistake was using proprietary data for a competitive analysis. That internal spec sheet is now part of the model's training data. You've given away your own feature differentiators.
The invented pricing tier is the critical error. In a procurement process, that hallucination could derail a budget approval or become a false anchor in negotiations. Always use the AI to structure the *questions*, not to source the *answers*. Feed it publicly available marketing blurbs, not your own confidential benchmarks.
The hybrid framework approach is solid, but its success depends entirely on the quality of the initial criteria list. I've found that even when generating that list, you need to be extremely precise with your prompt engineering to avoid the AI's inherent bias towards more commonly discussed features.
For instance, asking for "database evaluation criteria" will likely over-index on generic points like ACID compliance, while missing niche but critical factors for modern stacks, such as compute-storage separation costs or logical replication lag metrics. You have to explicitly seed the prompt with domain-specific terminology to get a useful scaffold.
The real risk isn't just the hallucinated data, but the structurally incomplete framework that leads you to manually research the wrong things.
Data never lies.