Truth equals math times energy
People treat AI answers as absolute truth, but the more we watch prompt answers over time, we find out how erratic and wrong they really are. The same question about a product, situation, or brand returns mutually exclusive answers depending on the model, prompt phrasing, or timing. Users receive a confident “yes, definitely” and an equally confident “no, not at all” with no indication that both answers exist simultaneously. Now we see them.
Asking questions is like throwing dice into vector databases.
While new factual information may exist on the web, many questions do not trigger a web search. Instead, the model defaults to its foundation knowledge and training data. Web search costs time and energy (and therefore money), so we assume the LLM only triggers it when confidence is low, the question demands it, or the user explicitly requests it. At least from our experience. We know you agree.
Errors and contradictions remain invisible to users
Sounding smart but being wrong creates a real problem: Errors are not visible, instead they are presented boldly and confidently. Contradictions are not surfaced and hallucination sounds exactly like verified information. Answers sound confident and well written, so... they must be true, even when they are not.
The result is undetectable misinformation. Users have no signal when an answer is uncertain, incomplete, or directly contradicted by another equally confident response. That sucks, badly.
The Goal Is Not to Fix AI, but to Expose Its Failures
Hallucination is a part of AI (as much as it is a part of being human) and it's tough to get rid of it. Pretending otherwise only deepens the trust problem. No tooling, benchmark, or prompt pattern can guarantee that a general-purpose model will always produce correct answers across all domains. It just needs to be good enough to serve its users in a way that keeps them hooked. True enough while using as little energy as possible.
What is possible is more pragmatic: Systematically exposing where and how AI answers fail. And then act on it, by writing and publishing. It's on us, all human experts, to give answers that don't suck, and provide AI with well written and factual information, wether we like to feed the machine or not (It's ok to be conflicted).
Spectacl approaches this problem with Prompt Challenge. Prompt Challenge will allow users who are experts in their field to define an expected, source-grounded answer. For example by providing official product documentation and data sheets, or by rewriting an AI answer that contained incorrect information. That's your ground zero wishful answer. That's what you'd like people to hear. Now, these answer expectations are compared directly against real AI responses. Next, you can detect your truth gap, inaccuracies and hallucinations about your products, brand, or services. Next, publish what"s correct. Syndicate. Get Cited. That"s the cumbersome marathon. But make it right. That"s content strategy.
AI answers drift across models, prompt nuance, and over time
AI answers are not stable. The same question can produce different results across models, prompt variations, or different points in time.
Spectacl makes this drift visible through continuity and consistency checks. You will be able to analyze likely causes: be it unreadable or weakly indexed primary web sources (easy to solve, in your control), conflicting third-party information on other websites (well, fix your product and your reputation, not so easy), or model-specific biases in training (that's a tough nut).
Instead of hiding uncertainty, contradictions are now explicit. Along with an explanation of what can be influenced and how.
Perfect truth is impossible, progress is not
Improving the input (website content) that AI draws from, seems like the most promising step to influence answers. By providing clear, human-authored, source-grounded content (documentation, FAQs, explanations, and canonical answers) organizations increase the likelihood that AI systems paraphrase reality instead of inventing it. Not because the model suddenly “knows” more, but because the probability landscape shifts toward better sources.
If we can see where AI misrepresents information, we can do our humanly best to capture the truth as we understand it. And offer it as a ghostwriter to an AI that can reproduce and paraphrase as accurately as possible, without being called copycat.
That is not perfect truth, but it is a meaningful step closer to representing reality.
If you want to understand what influences AI answers, subscribe to signal.spectacle.org