LLM

LLMs break traditional observability. Log tokens, trace multi-step chains, attribute costs, monitor quality with evals, and handle privacy deliberately.

Three seconds will still feel like forever to your users. Make sure they never have to wait that long staring at a spinner.

Your LLM costs more than you think - here's the full picture

RAG is the dominant AI application pattern. Chunking is the #1 pain point.

Getting reliable structured data from LLMs is harder than it looks

128K tokens doesn’t mean you should use 128K tokens

Your LLM application will hit rate limits. Here's how to handle it gracefully with exponential backoff, token buckets, and priority queuing.

How models learn semantic relationships in language

The survival of your AI app depends on your token economics

The Invisible Tax of AI Tokenization: Why Non-English Speakers Pay More

How your prompt becomes vectors, attention, and text in milliseconds

How words find their place in semantic space through push, pull, and careful trade-offs

How a simple observation about language created modern AI's understanding of meaning

The 35-year engineering journey that turned a linguistic insight into AI's foundation

How I stopped seeing vectors as magic and started seeing them as the geometry of meaning itself.

Should you build a custom tokenizer? A production decision framework

Why Swahili speakers pay 1.8x more than English speakers—and how to fix it

From GPT to BERT to T5 - the algorithms that learned to speak 100 languages

How the invisible step before training shapes model intelligence, costs, and fairness.