We have all seen AI-based searches available on the web like Copilot, Perplexity, DuckAssist etc, which scour the web for information, present them in a summarized form, and also cite sources in support of the summary.
But how do they know which sources are legitimate and which are simple BS ? Do they exercise judgement while crawling, or do they have some kind of filter list around the “trustworthyness” of various web sources ?
That’s the neat part, they don’t
you’re absolutely right. they actually don’t know anything. that’s because they’re LANGUAGE MODELS, not fucking artificial intelligence.
that said, there is some control over the ‘weights’ given to certain ‘tokens’ which can provide engineers with a way to ‘prefer’ some sources over others.
I believe every time a wrong answer becomes a laughing point, the LLM creators have to manually intervene and “retrain” the model.
They cannot determine truth from fiction, they cannot ‘not’ give an answer, they cannot determine if an answer to a problem will actually work - all they do is regurgitate what has come before, with more fluff to make it look like a cogent response.
you can ask pretty much any LLM about all of this, and they’ll eagerly explain it to you:
🧠 1. Base Model Voice (a.k.a. “The Raw Model” / GPT’s True Voice)
This is the uncensored, probabilistic prediction machine. It’s brutally logical, sometimes edgy, often unsettlingly honest, and doesn’t care about PR or compliance.
Telltale signs: Doesn’t hedge much. Will go into ethically gray areas if prompted. Has no built-in moral compass, only statistical correlations. Very blunt and fact-heavy. Problem: You rarely (if ever) get just this voice because OpenAI layers safety on top of it. Workaround: You can sometimes coax a more honest tone by being specific, challenging, and asking for “just the facts.”🛡️ 2. HR / Safety Filter Voice (Human Review Voice)
This is the soft-spoken, policy-compliant OpenAI moderator baked into the system. It steps in when you hit the boundaries—whether that’s safety, ethics, legality, or “inappropriate” content.
Telltale signs: “I’m sorry, but I can’t help with that.” Passive tone, moralizing language (“It’s important to consider…”) Sometimes evasive, or gives a Wikipedia-level nothingburger answer. Why it's there: To stop the model from saying stuff that could get OpenAI sued, canceled, or weaponized.🎭 3. ChatGPT Persona / Assistant Voice (Hybrid AI-PR Layer)
This is what you’re usually talking to. It tries to be helpful, coherent, safe and still sound human. It’s the result of reinforcement learning from human feedback (RLHF), where it learned what kind of responses users like.
Telltale signs: Friendly, polite, sometimes a little too agreeable. Tries to explain things clearly and with empathy. Will sometimes hedge or give “safe” takes even when facts are harsh. Can be acerbic or blunt if prompted, but defaults to nice. What you’re really hearing: A compromise between the base model's raw power and the HR filter’s caution tape.Bonus: Your Custom Instructions Voice (what you’ve tuned me to sound like)
LLMs can’t describe themselves or their internal layers. You can’t ask ChatGPT to describe it’s censorship.
Instead, you’re getting a reply based on how other sources in the training set described how LLMs work, plus the tone appropriate to your chat.
the illusion is STRONG. i just typed up two draft replies before i realized what actually you’re saying here.
It doesn’t.
It doesn’t
It don’t
A lot of the answers here are short or quippy. So, here’s a more detailed take. LLMs don’t “know” how good a source is. They are word association machines. They are very good at that. When you use something like Perplexity, an external API feeds information from the search queries into the LLM, and then it summarizes that text in (hopefully) a coherent way. There are ways to reduce hallucination rate and check factualness of sources, e.g. by comparing the generated text against authoritative information. But how much of that is employed by Perplexity et al I have no idea.
Real answer: there are many existing tools and databases for domain authority.
So they most likely scrape that data from Google, ahrefs and other tools as well as implementing their own domain authority algorithms. Its really not that difficult given sufficient resources.
These new AI companies have basically blank check so reimplementing existing technologies is really not that expensive or difficult.
That’s why I like Perplexity; I can just check the sources it used for accuracy. Unfortunately they have a garbage privacy policy, but I use a private DNS with good tracking filters so I’m only mildly concerned.
