Temperature, System Prompts, and Why AI Has No Memory: The "Personality" of LLMs
Context
I used to think LLMs were like databases—you ask a question, you get the stored answer. But after playing with Temperature and System Prompts, I realized they are more like improv actors. They need a script and a "mood" setting to stay on track.
LLM is Stateless. It’s like a gold-fish; every time you hit "Enter," it’s seeing you for the first time. To make it "remember," we have to feed it its own history. we can control its "vibe" using Temperature and give it a permanent identity using System Prompts.
Temperature: The Creativity Dial
Low Temperature (0.1 - 0.3) : The AI is boring but reliable. It picks the most likely word every time.
High Temperature (0.7 - 1.0+) : The AI takes risks. It picks less likely words, leading to "creative" (and sometimes weird) outputs.
System Prompts: The Background Instructions
It’s a hidden instruction we send before the user even types a word.
Example: "You are a professional React Native expert. Keep answers short and use TypeScript."
The AI uses this as a North Star for every following response.
Stateless Nature: The "No Memory" Problem
LLMs don't have a database of your past conversations. If you say "Hi, I'm Govind" in Prompt 1, and "What's my name?" in Prompt 2, the LLM will say "I don't know."
The fix: We have to manually send the previous chat history back to the model with every new message.
In mobile apps, Statelessness is a challenge for local storage. Since we have to send the whole conversation history back every time to keep the AI "smart," our local state management becomes the AI's "memory." We have to be careful—sending too much history will hit that Context Window limit.
Building with AI is less like traditional programming and more like managing a very talented, very forgetful intern. You have to give them clear instructions (System Prompts) and decide how much caffeine they’ve had (Temperature).
