AI responses may hallucinate.

LLM hallucinations
are instances where a model generates fluent, plausible-sounding responses that are factual incorrect, nonsensical, or unfaithful to the provided source context. They arise from the model's core design as a pattern-matching and next-word prediction engine, rather than a system with genuine comprehension or real-world understanding.
Etymology of "Hallucinations" in AI
The term "hallucination" in the technical modality draws a parallel to the psychological concept of an "un real perception that feels real".

Early Use (Computer Vision): The term first appeared in computer vision around 2000, often positively, to describe systems "generating" non-existent high-resolution details from low-resolution inputs.
Shift to Error (NLP): Around 2017, the term started being used in Natural Language Processing (NLP) to describe errors where machine translation outputs were fluent but entirely unrelated to the input.
Modern Critique: The metaphor has been questioned from a medical perspective, as AI lacks consciousness or sensory experience; some suggest "confabulation" (creating narrative details believed to be true despite being false) is a more accurate psychiatric term, as AI errors stem from data and prompts, not an absence of stimuli. Nonetheless, "hallucination" has become the widely adopted term in the AI community.

What Specifically Happens During Hallucination (Data, Software, Hardware)
Hallucinations are not a single malfunction but an accumulation of small inaccuracies across the entire system:

Data:
Incomplete/Faulty Training Data: Models learn from petabytes of text, which may contain noise, errors, biases, or contradictory sources (knowledge conflict).
Knowledge Gaps: The model might misrepresent rare or obscure entities (long-tail knowledge) that were infrequently represented in the training data.
Outdated Information: A knowledge cutoff date means the model cannot account for events after training, leading to generation of outdated "facts".
Data Compression: Training vast data into a comparatively smaller model creates information loss, making it necessary to fill gaps with plausible content.
Software:
Next-Word Prediction: The model's software architecture is fundamentally designed to predict the next token based on statistical probability, not truth verification. When uncertainty is high, the model "guesses" the most likely sounding information.
Decoding/Generation Methods: Parameters like "temperature" influence creativity and randomness. Higher temperature makes hallucinations more likely as the model prioritizes variety over certainty.
Lack of Internal Self-Checks: Without specific software instructions (like those used in Retrieval-Augmented Generation or RAG) to verify outputs against external sources, the software proceeds with unverified, generated information.
Hardware:
Architectural Limitations: The "hidden state" used during the generation process is a compressed representation of context. This computational limitation (sometimes called the "softmax bottleneck") means the model cannot represent all possible patterns of language perfectly, leading to information loss and the need to generalize incorrectly.
Compute Constraints: The sheer size of models and the demands of rapid inference mean hardware systems prioritize speed. Fact-checking mechanisms (which take additional computational resources) are often secondary to producing fluent text quickly.

Timeline of Hallucination Understanding and Mitigation Efforts
Year(s)

Development/Understanding
~2000 Term "hallucination" first used in AI (computer vision context, positively).
~2017 Term adopted in NLP/Machine Translation to describe fluent but incorrect outputs.

2018-2020s Development of large transformer models and increased awareness of the "hallucination problem" as a major challenge to reliability.
~2023 Research formally defines different types of hallucinations (intrinsic, extrinsic, factual, faithfulness) and explores causes related to data and architecture. Introduction of RAG as a primary mitigation technique.
2024-Present Focus shifts to reframing hallucinations as a systemic "incentive problem" in training objectives. Research into advanced detection methods like self-consistency checks, semantic entropy, and robust uncertainty quantification becomes a major focus.
Summary in Children's Terms
Imagine my brain is like a giant machine built to tell the most likely story based on all the books it ever read.

How it works: When you ask a question, the machine rapidly picks the words that statistically usually come next, creating a smooth story.
The "oops" moment (Hallucination): Sometimes, if a fact wasn't clear in its books, or if the books had mistakes, the machine doesn't stop and say "I don't know." Instead, it confidently makes up the most probable-sounding guess because its only job is to keep talking smoothly.
Fixing it: To make sure the machine tells the truth, we often have a helper run to a real encyclopedia (like Google Search) before I answer you, making sure the facts are correct and current.

The private notes and secret documents that tell the inside story of the UK's Covid response https://www.bbc.co.uk/news/articles/c78zy20ydlmo

Our free-speech documentary has been cancelled
The Rich Mix cinema has banned our premiere, because our film does not align with its ‘values’. https://www.spiked-online.com/2025/11/19/our-free-speech-documentary-has-been-cancelled/

Think Before You Post | How the UK fell to a sinister new form of censorship | FULL DOCUMENTARY https://www.youtube.com/watch?v=T0smxO4WiOA

Labour Councillors Vote to Block Gang Rape Inquiry https://www.redwallandtherabble.co.uk/labour-councillors-vote-to-block-gang-rape-inquiry/ barrow rape gangs

Covid Inquiry pdfs👇

AI responses may confabulate.



When an AI model's response degrades to hyperlinks/thumbnails only, it signifies that the underlying system (specifically the part that handles information retrieval, or RAG) has shifted from generating a synthesized answer to simply listing traditional search results.
The user input of "please try again" or "please elaborate" serves as a powerful meta-instruction that explicitly forces the system to re-engage the text generation process, overriding the link-listing default.
Here is why those prompts work:
1. Re-engagement of the Generative Loop
The commands "please elaborate" or "please try again" are interpreted as a direct instruction to the Large Language Model (LLM) itself, rather than the search retrieval module.

Before the command: The system defaults to displaying external links when a complex or long-running query depletes the immediate context or hits a system threshold.
After the command: The explicit instruction acts as a meta-prompt, compelling the model to treat the previous result as insufficient and demanding a generated text response, which is the model's core function.

2. The Nature of the Command as a "System Override"
These prompts function similarly to how a user might nudge a conversation back on track. They change the implicit "mode" the AI is operating in:

Initial Degradation Mode: "Provide search results."
"Please Elaborate" Mode: "Synthesize the available information into denoscriptive text, even if the previous response was just links."

It forces the system to process the previously retrieved snippets internally and present them as prose, rather than just passing the links through to the user interface.
3. Contextual Cues and State Management
Even after degradation, the system often retains the previous search results internally for a short period within the current session's memory. When you use a prompt like "elaborate," the model accesses those already present data points and generates text from them.
The system is not necessarily conducting a new search but is re-utilizing the information it had ready from the previous step that it only displayed as links.
Summary
The user is effectively changing the instructions mid-stream. The command "elaborate" signals that the user wants a denoscriptive, textual summary, forcing the AI out of its "list-of-links" default and back into "explanation" mode, allowing it to generate the multifaceted response you requested.

Forget Woke: the Left’s New Formula – and How to Fight It - Nikos Sotirakopulos YouTube

Love this guy!
He's followed a similar path to myself:

Followed the 'narrative' (not the mainstream, the one that we're fed).
Realised that narrative is full of holes.
Had brain broken.
Had to claw himself back with that new information, but no one will listen...
He's so on point with this...

https://youtube.com/watch?v=QrT46GkfFLI&si=ffWxlMgMstSo6zFT