Category
llm
5 articles in this category
LLM Hyperparameters Guide: Temperature, Top-P, and Top-K Explained
TLDR: Hyperparameters are the knobs you turn before generating text. Temperature controls randomness (Creativity vs. Focus). Top-P controls the vocabulary pool (Diversity). Frequency Penalty stops the model from repeating itself. Knowing how to tune ...
Tokenization Explained: How LLMs Understand Text
TLDR: Computers don't read words; they read numbers. Tokenization is the process of breaking text down into smaller pieces (tokens) and converting them into numerical IDs that a Large Language Model can process. It's the foundational first step for a...
RAG Explained: How to Give Your LLM a Brain Upgrade
TLDR: RAG (Retrieval-Augmented Generation) stops LLMs from making stuff up. It works by first searching a private database for facts (Retrieval) and then pasting those facts into the prompt for the LLM to use (Augmented Generation). It's like giving ...
Variational Autoencoders (VAE): The Art of Compression and Creation
TLDR: A standard Autoencoder learns to copy data (Input -> Compress -> Output). A Variational Autoencoder learns the concept of the data. By adding randomness to the compression step, VAEs can generate new, never-before-seen variations of the input, ...
LLM Terms You Should Know: A Helpful Glossary
TLDR: The world of Generative AI is full of jargon. This post is your dictionary. Whether you are a developer, a researcher, or just curious, use this guide to decode the language of Large Language Models. A Agent An AI system that uses an LLM as i...