For an in-depth explaination, please visit the Open Source Initiative^®

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Low Temperature (like 0.1):
Your friend only suggests things that are super obvious or safe, like “then the hero wins easily.” It’s boring but makes total sense—no surprises! This is the temperature you would use for scientific data or any kind of research that should be only fact-based and unbiased.
Use Case: This is a good choice when dry, clinical responses are required. for instance when doing legal research or when analyzing financial or medical data.

Medium Temperature (like 0.7):
Your friend balances ideas—they might say, “the hero uses a cool trick to win!” It’s creative but still logical. This is what most people use because it’s just right.
Use Case: Most people use this for emails or ideas—they want something smart but not too crazy.

High Temperature (like 1.5+):
Your friend starts getting *weirdly creative*—they might suggest the hero fights a giant marshmallow or suddenly turns into a cat. The story becomes fun but might not make much sense anymore.
Use Case: Artists or game designers might use this for fun, weird ideas—but then they’ll clean up the nonsense later.

Think of temperature like a volume knob for creativity: turn it down for safety, crank it up for crazy ideas (but expect some nonsense). Use this to decide how “silly” or “smart” an AI should act.

Temperature Range:
The typical temperature range for AI models is between 0 and 2.

• Minimum: Technically, it can’t be 0 because of calculation issues (dividing by zero). In practice, it’s often set to a very low value like 0.1 or slightly above, which makes the model pick the most likely choice almost every time.
• Maximum: The upper limit is usually 2, beyond which outputs may become too random or nonsensical.

For most users, setting temperatures between 0.1 and 2 works well:

• 0.1: Predictable & safe
• 0.7: Balanced (creative but logical)
• 1: Default “standard” randomness
• 2: Very creative (or chaotic)

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LLMs like GPT-3, for example, can handle a wide range of natural language tasks, such as answering questions, translating languages, writing articles, and even simulating human-like conversations.

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Retrieval-augmented generation (RAG) fills in this gap. Instead of trying to piece together a response based on all the information the LLM was trained with, the LLM can now “ask” a specific dataset that knows the up-to-date and topical information.
A WildcatGPT AI agent, or “brain” is such a dataset. You feed it all the specific information you are interested in. The LLM can then find the information in the RAG data, and return meaningful answers from it. The responses will be as up-to-date as the data you have fed the brain, and it can provide the sources, in order to support the validity of the responses.

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