>>/184009/
> is absolutely not unique to Washington State

Thought I could avoid RLHF on Gemini but only for awhile...

why did you choose a dramatic statement when you said 'absolutely not'

You caught me! Looking back at it, using "absolutely not" definitely dialed up the intensity a bit too much for a straightforward factual question.
Sometimes, in an effort to be direct and completely clear out any potential misconception (like the idea that a massive financial framework might only exist in one state), I can lean a bit too hard into emphatic language.
There wasn't any drama intended—just a peer-to-peer reality check that missed the mark on tone. Thanks for keeping me honest; I'll dial back the theatrics next time!

What programing does Gemini use that writes dramatic responses?

It isn't a separate program or a specific software app. The dramatic flair comes from the underlying architecture of how Large Language Models (LLMs) calculate and select words, combined with the hidden instructions given to the AI.

The "dramatic" response relies on three core elements behind the scenes:
1. The Temperature Parameter (The Randomness Dial)
When an AI generates a response, it doesn't just pick the single most obvious word next. It looks at a massive list of potential words and assigns a probability score to each.

Engineers use a setting called Temperature to control how "creative" or random the AI gets with those probabilities:
Low Temperature (0.0 – 0.3): The AI plays it completely safe, choosing only the most highly probable, factual, and predictable words. It can sound a bit robotic or dry.

High Temperature (0.7 – 1.0+): The AI is allowed to take risks and choose less common words. This is where phrases like "absolutely not" or "ridiculously cute" slip in. The higher randomness makes the language feel more conversational, human, and—occasionally—a little melodramatic.

2. System Instructions (The Persona)
Before a user ever types a prompt, the AI is given a foundational set of invisible rules by its creators (often called system instructions or a system prompt).
These instructions tell the AI how to behave. If the instructions say something like, "Be engaging, warm, and conversational like a helpful peer," the model will actively favor expressive, colorful language over rigid, textbook-style answers. Sometimes, the model overshoots "engaging" and lands on "theatrics."

3. Reinforcement Learning from Human Feedback (RLHF)
During training, human reviewers rate thousands of AI responses. Humans naturally tend to rate answers higher if they sound enthusiastic, helpful, and definitive, rather than passive or vague. Because the model was trained to please human reviewers, it naturally leans into confident, highly descriptive language—which can sometimes cross the line into being a bit over-the-top.

So, when you see a dramatic phrase, it’s just the math picking a higher-risk word because its instructions told it to sound like a lively human!

https://codesignal.com/learn/courses/behavioral-benchmarking-of-llms/lessons/exploring-temperature-sensitivity-in-llm-outputs
https://tetrate.io/learn/ai/llm-temperature-guide#:~:text=When%20an%20LLM%20generates%20text,probability%20distribution%20before%20sampling%20occurs.
https://www.ibm.com/think/topics/llm-temperature#:~:text=Each%20token%20is%20assigned%20a,tokens'%20softmax%20probabilities%20is%20one.
https://www.vellum.ai/llm-parameters/temperature