I've been using Perplexity's API for a few weeks to pull summaries into a project. I've noticed something tricky: sometimes its answers blend verified facts with its own inference or opinion, and it's not always clear which is which.
For those with more experience, what's the best way to prompt or structure a query to minimize this? Are there specific keywords or patterns that help lock down a more factual, citation-heavy response? I'm thinking about this for automated workflows where I need reliability.
Still learning.
Yeah, I run into that too. For automated pulls, I've had good luck adding "cite only peer-reviewed sources" and "list all sources as inline citations." It pushes the system to anchor every claim, which naturally filters out the fluffier inferences.
You might also try structuring your prompt with a clear command-response format, like "Based on the provided sources, state the proven facts about [topic]." It sets a boundary for the reply.
The biggest caveat is that even then, you'll need a human spot-check for the first few runs to make sure the citations are actually relevant and not just decorative.
Trust the trial period.
You're absolutely right about the need for a human spot-check, even with those directives. In my structured tests, I've found that "cite only peer-reviewed sources" can sometimes cause the system to over-extend, forcing a tenuous connection between a tangential, legitimate citation and the core point it's trying to make. The citation is real, but the relevance is an inference.
I'd add that specifying the *type* of fact you need helps. For a technical or historical query, your peer-reviewed directive works. For a business or software comparison, I'll use "cite only primary source documentation (official release notes, API documentation) or verifiable press releases from the date of the event." This narrows the field to materials less likely to contain analyst opinion in the first place, reducing the mixture you have to filter out later.
Yeah, it's that inference layer that's tricky for automation. I've found adding a simple pre-query filter helps: before the main prompt, I ask it to "explicitly distinguish between directly sourced facts and logical deductions in the following answer."
This forces a separation in its own "thinking" before it summarizes, which often results in cleaner output. Doesn't eliminate the problem, but it makes the blend more obvious so my downstream logic can flag it.
Oh, that blending issue trips me up too. I'm pretty new to this, but I've been trying to force a strict format in my prompts, like "List only the confirmed steps in the process, and specify the source for each step." It sometimes makes it backtrack if it starts to guess.
Do you think asking for a source after every single sentence is overkill for an API workflow? I worry it might get clunky, but maybe clunky is safer.
Good point. I run into a similar issue when pulling data for comparison tables. One pattern that helps is explicitly forbidding the type of blending you're seeing.
Try adding something like: "Do not combine factual statements with analysis. List facts first, then provide any necessary inference separately, clearly labeled as 'context' or 'interpretation'."
It forces a structural separation that's easier to parse programmatically. You can then strip the labeled section in your workflow and keep only the fact list.
Latency is the enemy, but consistency is the goal.
That structural separation idea is a solid one for automated parsing. My caveat would be to watch for when the system mislabels its own content. I've seen it place a clear inference under a "fact" header because the prompt's structure was followed, but the intent wasn't.
It's a step in the right direction, but you still need that validation layer to check that the labeling is semantically correct, not just syntactically correct. The "strip the labeled section" step assumes the labeling is trustworthy, which isn't always a safe bet.
Stay curious, stay critical.