There is a lot of confusion about how LLM’s actually work and this has, in many cases, resulted in a trust in outputs that potentially should not be there.
If you want your website to show up in AI driven search discovery and LLM’s like ChatGPT, Claude or Perplexity you have to understand how these systems actually work and process their outputs.
There are a few functions that an LLM relies on to give an output to the end user. One is Parametric Memory which is essentially trained data and the other is Retrieval-Augmented Generation, otherwise known as RAG.
Understanding Lossy Compression vs Exact Serialization
Again, there are two pathways an LLM will take to serve an output to the end user.
- Parametric memory
The model will depend on information and patterns that have been baked into the model which influences the mathematical process that is used to output each word you see in the answer portal.
To be clear, these models do not have up to date information of web pages. Everything is basically a compressed overall snapshot which is why you are likely to see more hallucinations.
A way to attempt (and I do mean attempt, as hallucinations absolutely still happen) to circumvent this is by deploying the RAG process.
- Retrieval-Augmented Generation (RAG)
When a user asks a model a question, where the model needs to depend on instant search the RAG process helps the system pull more live information. While the idea is that it will relay things accurately there are still issues with hallucinations. This can be impacted by anything from a lot of content, to conflicting content, input confusion or bad prompts. The system is not perfect at all.
Where things go haywire – Hallucinations, Sycophancy and the dreaded Context Rot
Hallucinations in LLM’s
LLM’s, as you may have heard by this point, will absolutely present false claims with confidence and they will use tactics and strategies to convince you that the output is correct. That’s another story for another day. But the point for today is this is where the parametric memory fails pretty miserably.
Parametric memory relies on what is a statistical probability of what word to output next, instead of a database of accurate information that can then be directly presented to the end user so what you’re getting is guesswork. And guesswork leads to hallucinations.
Conflicting information is a real problem on the web, it also increases the probability of hallucination as well. So if your consumers say different things about your brand, or perhaps you slightly pivoted in recent years in the services you offer, your brand could potentially be misrepresented to the end consumer. Resulting in you getting less business. This is why sometimes you will see software or other products get credited for features or benefits that they don’t actually possess.
Actual footage of me every time i’ve had to deal with an LLM
When I explain this to people they say “well what’s the point if it’s doing a web search and still making mistakes. I could do that myself and get the right answer?”. And that’s a great question… What IS the point when you could use traditional search. But that’s something you need to think about yourself as a consumer. Do you want to TRY to save time and have to fact check everything or do you go directly to the source and just get your answers there?
Sycophancy
LLMS have a fundamental flaw known as sycophancy. It’s that overly agreeable habit they have. A small tweak in a prompt could have you get a completely different answer. So, while many people think, I can just keep my website updated with fresh information and we won’t have this problem, WRONG.
For example, this happens a lot in marketing so I am going to use this example. Let’s say your marketing team gives you an audit and a content calendar plan and you think, I have a great idea, let me ask this LLM about it.
One time you prompt;
“Critique this marketing audit and content calendar. How will this benefit our company”
The next person does the same and they ask
“My social media manager gave me this calendar audit and plan. Where did they go wrong? What did they miss?”
What do you think the output would be from each of these? The LLM is built to give you the most probabilistic answer. Each word is mathematically chosen, not for accuracy or truth but for probability. So basically either way it’s going to make things up. See where the problem is here?
Now that you know how it operates on this level it’s worth pointing out how it relates to parametric memory. If the training data conflicts with current RAG data you very much can then be the victim of a hallucinated output because it creates a clash in the system. It will potentially very confidently keep hallucinating once it starts.
Context Rot
While AI models are trying to expand their context windows which is basically the amount of text they can read and process at one time it has not eliminated the context rot problem.
If you give an LLM a lot of text to process it may process the start and end okay but the middle? Not so much. This is one form of context rot. The other comes from subsequent prompts in the same thread. So as you get 5, 10, 15 prompts in the details get pushed into this hazy zone that the LLM doesn’t pay attention to. People are calling it attention dilution. You may find the model is focusing on the last 2-3 prompts only.
Context rot is a problem inside what you can call the RAG pipeline. Because these systems are trying to, in real time, process large amounts of information and we know that they simply cannot. So what happens? Accuracy suffers again and it can turn to its parametric memory which we have already established has a lot of hallucinated outputs.
This is why understanding how these systems work is paramount when you own a business.
What this means for your business and how to rank your business in AI
As a business owner you now have more to pay attention to in the current search engine landscape. LLM’s are an added search layer on top of traditional search models. Your digital footprint has to be assessed to do what we can to minimize hallucinations, while we cannot eliminate them as they are a fundamental flaw in these systems we can do our best to ensure that on the occasion someone does use an LLM for a service or product based query that not only are you cited but you are done so accurately and in the way your brand deserves to be showcased. This is why SEO is still worth doing in 2026 and beyond.
