How input, output and cached tokens are billed, a worked monthly estimate for a support bot, and how much prompt caching, smaller models and output limits actually save.
#llm#cost#prompt-caching#api
Trailing commas, missing brackets, wrong fields? Here is how prompting, JSON mode, structured outputs, schema validation and automatic repair fit together — from least to most reliable.
#llm#json#structured-output
How tokenizers work, how tokens relate to words, why non-English text often uses more tokens, what counts toward the context window, and practical ways to cut token usage.
#llm#token#tokenizer
Chunking is the first gate of RAG quality. Concrete examples of chunk size, overlap, structure-aware splitting and metadata — and how to validate your choices with data.
#rag#embedding#llm
