📖 LLM Guides
In-depth tutorials covering every aspect of Large Language Models — from basic concepts to advanced techniques.
What is a Large Language Model?
A beginner-friendly explanation of how LLMs work, their architecture, training process, and why they matter.
Transformer Architecture Explained
Deep dive into the Transformer model — self-attention, positional encoding, multi-head attention, and the encoder-decoder design.
Attention Mechanism Explained
How attention works in neural networks — from basic concepts to multi-head self-attention in Transformers.
What is Tokenization?
How LLMs break text into tokens — BPE, WordPiece, SentencePiece, and why tokenization matters.
What are Embeddings?
Vector representations of meaning — how words and concepts become numbers that machines understand.
What is RLHF?
Reinforcement Learning from Human Feedback — how LLMs are aligned with human preferences.
LoRA Fine-tuning Guide
How to fine-tune large language models efficiently using Low-Rank Adaptation.
LLM Quantization Explained
Model compression techniques — INT8, INT4, GPTQ, AWQ, and how to run LLMs on consumer hardware.
What is KV Cache?
The key optimization for LLM inference speed — how KV cache works and why it matters.
Run LLM Locally Guide
Step-by-step guide to running LLMs on your own machine using Ollama, llama.cpp, and more.
What is RAG?
Retrieval-Augmented Generation explained — how to ground LLMs with external knowledge for accurate answers.
Vector Database Explained
How vector databases power semantic search and RAG — Pinecone, Weaviate, Chroma, and more.
AI Agents Explained
How LLM-powered agents work — tool use, function calling, planning, and autonomous execution.
LLM Hallucinations
Why LLMs make things up, how to detect hallucinations, and strategies to reduce them.