Tokenization quietly shapes context limits, cost and code generation — and most AI engineers skip it
mdancho84 · x · 2026-09-11
mdancho84 argues that data scientists moving into AI engineering skip a foundational lesson: LLMs see tokens, not text.
Tokenization quietly affects almost everything that matters when building real systems:
- Context window limits and API cost
- Code generation
- Numbers and identifiers
- Multilingual performance
- Embeddings and chat templates
- Model training
He also debunks common misconceptions: BPE is not automatically byte-level, SentencePiece is not itself a tokenization algorithm, and token IDs aren't semantic. The topic looks like an implementation detail until you're debugging a system and realize the model isn't receiving information the way you assumed.
More from Models
- DeepSeek at 213 tok/s and 1/10 the Price: Does the Intelligence Gap Still Matter for Coding? — DigZealousideal5298 · 2026-09-12
- Dev Fine-Tunes Qwen3.8-27B on 125K Real Conversations to Kill the AI Assistant Vibe — kvyb · 2026-09-12
- Muse, Instinct and Grok Bot 'would be best products of the year' in any other year — jeff_weinstein · 2026-09-12
- Microsoft launches MAI-Transcribe-2: single multilingual transcription model with diarisation and timestamps — mustafasuleyman · 2026-09-12
- GPT-6 and Fable 5.1 both fail basic physics in 3D scene building test — ZhitingHu · 2026-09-12
- LiveBench agentic coding eval questioned: outlier score rests on 4 Python issues — teortaxesTex · 2026-09-12