Clear explanations of ML concepts, algorithms, and architectures.
A clear, practical explanation of how LLMs work under the hood — tokenization, transformer architecture, attention mechanisms, and the fundamental limitations that no scaling can fix.
A practical guide to RAG systems that ground LLM responses in real data, covering pipeline architecture, chunking strategies, embedding models, reranking, and common failure modes.
A decision framework for choosing between fine-tuning, RAG, and prompt engineering based on your specific use case and constraints.
An explanation of multimodal AI systems that process text, images, audio, and video, with practical applications and limitations.
A practical explanation of vector databases: what they are, when you need one, and how to choose between the options available.
A practical framework for evaluating AI models on your specific task. Covers building evaluation datasets, production metrics that matter, and when to trust LLM-as-judge.