# 10 Confusing LLM Concepts, Explained Simply

DevFeed: [10 Confusing LLM Concepts, Explained Simply](<https://devfeed.tech/articles/10-confusing-llm-concepts-explained-simply-18351.md>)

Original publisher: [Read original article](<https://levelup.gitconnected.com/10-confusing-llm-concepts-explained-simply-031246b8ea34?source=rss-f10e9a50984a------2>)

Author: Dr. Ashish Bamania

Published: 2026-06-01T15:52:18Z

Content type: tutorial

Language: en

Sources: [Dr. Ashish Bamania](<https://devfeed.tech/sources/dr-ashish-bamania.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [coding](<https://devfeed.tech/topics/coding.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [coding](<https://devfeed.tech/tags/coding.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [math](<https://devfeed.tech/tags/math.md>), [programming](<https://devfeed.tech/tags/programming.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [technology](<https://devfeed.tech/tags/technology.md>), [tpu](<https://devfeed.tech/tags/tpu.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This tutorial introduces LLM concepts including on-policy and off-policy learning. It explains how models generate, score, and learn from responses, including the use of GRPO, teacher models, and datasets. The supplied excerpt also identifies CPU, GPU, TPU, pruning, and quantization as covered topics.

## Source excerpt

The role of CPU/ GPU/ TPU in LLM workflows, Pruning, Quantization, and more. Continue reading on Level Up Coding "