Master AI engineering concepts
AI fundamentals simplified,
one concept at a time.
Built for engineers moving from “I can call an API” to “I can own this in production.”
Software engineers
Tech leads
Product builders
AI Fundamentals Simplified
Essential concepts.
Beginner
What is a large language model?
10 min ReadWhat is a tokenizer?
10 min ReadTokens in large language models
7 min ReadEmbeddings and vector representations
8 min ReadVector representations for AI systems
9 min ReadParameters and weights in AI models
9 min ReadPrompt engineering
10 min ReadMultimodal AI
10 min ReadPre-training AI models
7 min ReadOpen-weight AI models
6 min Read
Intermediate
LLM architecture and attention
8 min ReadInference — how models generate output
10 min ReadFine-tuning a pre-trained model
10 min ReadEncoder-decoder models
7 min ReadTransformers and BERT
10 min ReadContext engineering
10 min ReadPost-training AI models
6 min ReadHuman-in-the-loop AI systems
8 min ReadAttention mechanism
9 min ReadRecurrent neural networks (RNNs)
7 min ReadVector distance and similarity metrics
8 min ReadCosine similarity
9 min Read
Advanced
Latest from the blog
Blog posts.
Why GPUs Became the Engine of Modern AI
How graphics hardware, matrix multiplication, CUDA, and Transformers came together to make GPUs essential for modern AI.
Read articleWhy Does AI Writing Use So Many Em Dashes?
How edited training data, preference tuning, and the default assistant voice made em dashes common in AI-generated writing.
Read articleFrom Prototype to Production: A Reliability Checklist for LLM Applications
A practical checklist for turning an LLM demo into a reliable production feature with measurable behavior, safe boundaries, and useful observability.
Read article