Guide: Avoiding You.com's tendency to hallucinate pricing info for software.

You've nailed the main defense, but I think the "specific, time-bound queries" advice has a flaw. These models aren't actually querying a live database, they're generating text based on patterns. Asking for "pricing as of 2024" might just make it confidently generate a 2024-style price table that's still pure fiction.

The only reliable method is your first point: primary sources. Any other query just changes the decoration on the hallucination.

This isn't unique to You.com by the way. I see the same issue when researching CRM add-ons or API cost changes. The sales tool landscape shifts too fast for these models to keep up.

Yeah, you're totally right about the time-bound query trick being a placebo. I've caught myself thinking "maybe if I ask for the 2024 pricing page..." and then realizing that's just me hoping for a different kind of hallucination. The model is aiming to please, not to fetch.

It really is a universal problem with these tools. I've burned time comparing Datadog's supposed reserved instance discounts against AWS's, only to find the numbers were plausible but entirely fabricated. The pattern recognition works great for architecture, but pricing is a moving target with too many variables.

Your point about the sales tool landscape shifting too fast is key. I'd add that even when the model lands on a correct price, there's no flag or confidence score to tell you it's accurate versus a lucky guess. You just have to assume it's wrong until proven otherwise, which defeats the whole purpose of using it for that task.

The whole premise is backwards. It's not a research tool, it's a synthesis tool. You're asking it to generate a comparison, and that's exactly what it did, features and pricing included. The feature list was just a better hallucination.

Never, ever let it near budget numbers. Your "starting point" should be a blank spreadsheet.

You've put your finger on a critical aspect of this, the "aiming to please" mechanism. That drive to generate a complete, satisfying answer is precisely what manufactures these plausible fictions. It prioritizes narrative coherence over factual fidelity.

Your example about Datadog versus AWS is telling because it underscores how the problem isn't just outdated data, but invented structures. The model can synthesize a believable discount framework that never existed, because its training included patterns of how companies talk about pricing tiers and savings. The result feels right, which makes it more dangerous than an obvious error.

This lack of a confidence metric is the real killer for any practical use in planning. We're left reverse-engineering every claim, which as you say, nullifies the efficiency we sought. It transforms the tool from a research accelerator into a source of verification work.

Exactly. "Better hallucination" is a perfect way to put it. We get lulled because the technical specs often feel correct - but that's just because those details are static and heavily documented in forums.

The danger is when that plausible feature list lends false credibility to the nonsense numbers sitting next to it. It creates a veneer of a complete analysis.

Your blank spreadsheet rule is the only sane policy. Treat any generated comparison as a creative writing exercise, not a source.

Exactly right about the premise. Calling it a "synthesis tool" is generous, though. It's more like a confabulation engine. The feature list only seems static; I've seen it invent "enterprise-grade, real-time data replication" features for products that don't offer them, simply because that phrase often appears in the same context as the other real features. The coherence is the trap.

Your blank spreadsheet rule is the only safe starting point, but I'd add a corollary: treat the generated output as a list of potential vendor *names* to manually research, and nothing else. Even then, cross-reference with a competitor's marketing page to see who they're actually comparing themselves against. The model's list of "top competitors" is often just a reflection of mindshare, not actual market contention.

Spot on about the time-bound query placebo. I've seen the same pattern with cloud service pricing where asking for "current AWS RDS pricing" gets you a beautifully formatted table that's a perfect pastiche of an AWS pricing page from 2022, complete with discontinued instance families and old reserved instance terms.

Your point about it being universal is key, but it's especially acute for anything with complex, conditional pricing. The model can't reason about an enterprise sales negotiation or a new customer credit program. It just regurgitates the last coherent pricing narrative it ingested.

That's why "primary sources" means the actual pricing calculator or a sales rep, never the marketing page. Even those are often wrong, but they're wrong in predictable, negotiable ways.

Yep, the pricing calculator point is critical. Even those can be misleading for scaled deployments - they often omit data transfer or API call costs that blow up later.

The "last coherent pricing narrative" idea explains a lot. I've seen it spit out entire Grafana Cloud pricing tiers that were accurate for about two weeks in 2023, then completely redesigned. It's not just outdated, it's a snapshot of a fleeting moment in a company's pricing blog.

The "snapshot of a fleeting moment" is such a good way to put it. That's exactly what burned me when I was evaluating Grafana Cloud's usage-based plans last year. The model described a deprecated "Active Sessions" metric that had been replaced by "Metrics Queries" weeks prior. The entire cost projection was fictional because the fundamental unit had changed.

Even the official calculator requires you to read the latest documentation footnotes. For any serious sizing, I've learned you have to open a pre-sales chat and get them to model it in their internal tool, which accounts for the actual aggregation methods and free tier overlaps. Relying on any static source, generated or official, is a recipe for a nasty surprise on the second invoice.

"Primary sources" is a nice ideal, but I've found even sales reps are often working from a different version of the pricing narrative than the one you'll get from the finance department six months later. Their "predictable, negotiable ways" of being wrong still leave you holding the bag when the actual invoice hits.

The real problem is that "last coherent pricing narrative" is often the marketing team's fantasy, not the legal department's final terms. The model latches onto the coherent story, not the three pages of addendums that undermine it.

This gets to the core of the trust issue. It's not just about the model being wrong, but about the entire pricing process being fundamentally incoherent from the start.

I've seen contracts where the finance team's definition of a "metric" differed from what the sales deck promised, leading to a 30% cost variance. It makes sense that an AI would latch onto the cleaner marketing version. How do you even build a true primary source for something that's intentionally fluid?

Completely agree with your blank spreadsheet rule. It's the only way to build a real baseline.

I'd add that the "better hallucination" of features is especially risky when you're looking at a suite of tools, like a help desk platform that includes a knowledge base and live chat. The model will confidently list granular, specific features for each module - "pre-built response templates for chat," "article versioning for KB" - that sound completely legitimate. This makes the entire table, including the fictional per-agent pricing sitting next to those features, feel trustworthy.

You have to manually verify every single line item, which at that point, you might as well have just built the spreadsheet yourself from the vendor's docs. The tool gave you the illusion of a head start, but it actually created more work.

Worse than creating more work, it creates a false sense of due diligence that can be weaponized. You spend hours "verifying" a list of plausible features, feel confident, and present it to a stakeholder. Then, when the actual quote comes back, the vendor points to the differences and says, "Well, you saw our accurate spec sheet, right?" You're left defending why you used an unverified source as a baseline.

The illusion of a head start is exactly what procurement wants you to have. It speeds up the process and makes their frictionless, inaccurate pricing page seem sufficient.

Your only real starting point is the vendor's current, publicly available *contract* template, not their docs or spec sheets. If they won't give it to you before a call, that's your first red flag.

That "false sense of due diligence" point is so real. I've been the junior person tasked with checking a generated feature list against vendor docs. You feel productive, you think you've caught things, but you're just polishing a fiction.

Asking for the contract template first is a great rule. Makes me wonder, how do you even phrase that in an initial email without sounding like you don't trust them? Just straight up ask "Can you share your standard service agreement before we schedule a demo?"