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How does an LLM use GPU memory?
An LLM uses GPU memory for three things, the model weights, the KV cache, and some overhead, so the simple formula is VRAM = weights + KV cache + overhead.
How does RAG work?
RAG, or Retrieval-Augmented Generation, first finds the right information for our question and then gives it to the LLM, so that the LLM can write an accurate answer.
What is an AI Agent Engineer?
An AI Agent Engineer is an AI Engineer who specializes in building AI agents, which are systems built around an LLM that decide what to do next, use tools, and keep working step by step until a goal is reached.
How does flow matching train a generative model?
Flow matching trains a generative model by teaching it the direction and speed to move from a random noise point toward a real data point, like a photo. During training, it draws a straight line between a noise point and a real data point and asks the model to predict the movement along that line.
What is Policy Gradient? The REINFORCE Algorithm Explained
Policy Gradient is a way to train an AI agent by directly making the actions that led to good results more likely, and the actions that led to bad results less likely. REINFORCE is the simplest algorithm that does this.
How does a voice agent truncate its memory to only what the user actually heard?
A voice agent truncates its memory by finding out how much of its reply was actually played through the speaker before the user interrupted, and then cutting its saved reply down to only those words, so that its memory matches what the user really heard.
What is a Value Function? Q-Learning Explained Step by Step
A Value Function gives a number that tells an agent how much total reward it can expect from a situation, and Q-Learning is a method that learns these numbers for every situation and action by trial and error.
Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are neural networks built to understand images. They slide small pattern detectors over a picture to find edges, shapes, and objects, step by step.
What is Exploration vs Exploitation in Reinforcement Learning?
Exploration vs Exploitation in Reinforcement Learning is the choice an agent faces at every step: use the action it already knows works well (exploitation), or try a different action to learn something new that can work even better (exploration).