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Every time you ask an LLM a question
> Is it learning something new, or just using what it already knows?
that’s basically the difference between Training and Inference.
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Inference:
→ Give the trained model a new prompt
→ Use those learned weights to predict the next tokens
→ Generate an answer without updating the weights
→ Costs add up every time someone uses the LLM
Is ChatGPT a search engine? Many people say "No"
but I think that answer needs an update now..
Here's how I look at it:
Thread 🧵(1/n)
(3/n)
so now it's doing two jobs:
> search finds the information.
> the LLM makes sense of it and writes the answer.
and that's what makes modern ChatGPT so useful.
You tell your AI agent, "Don't forget this." Then, after 20 long prompts, it quietly starts forgetting what you said.
Have you been in that situation? We've all been there. Today, let's break down why it happens.
that’s usually a context window problem.
Thread 🧵 (1/4)
(3/4)
that is why this matters:
→ your prompt uses part of the window
→ the chat history uses part of it
→ uploaded docs use part of it
→ the model’s own reply uses part of it too
(4/4)
So a "bigger context window" doesn’t mean perfect memory
It just means the model can keep more text visible at once before older parts start falling out
LLMs don’t "remember" like we do but they read whatever still fits in the window
#LLM #AIEngineering #GenAI
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