Articles tagged "glossary"

Fine-tuning explained: when it beats prompting and when it doesn't

Fine-tuning trains a model on your data to shape behavior and output format. Here's when it beats prompting, what it costs in 2026, and when to skip it.

Jul 20, 2026 · 5 min read glossaryfine-tuningprompt-engineering

Function calling explained: how LLM tools work in production

Function calling lets LLMs trigger your code via JSON schemas. How the loop works, when to use it over plain prompting, and the pitfalls most builders hit.

Jul 10, 2026 · 7 min read glossaryfunction-callingtool-use

Embeddings explained: vectors, RAG, and which model to pick

Embeddings convert text into vectors that capture meaning. Here's how cosine similarity works and how to pick the right model for your RAG pipeline.

Jul 6, 2026 · 6 min read glossaryembeddingsrag

What is MoE? The sparse expert trick behind DeepSeek and Mixtral

Mixture of Experts models run only a fraction of their parameters per token. Here's why DeepSeek and Mixtral are cheap, and when MoE gets expensive.

May 22, 2026 · 7 min read glossaryfundamentalscost

1 token is not 1 word: LLM conversion rates that predict your bill

The exact token-to-word and token-to-character conversion rates for English, code, and non-English LLM input, plus a practical counting recipe.

Apr 27, 2026 · 6 min read glossarytokenscost

What is RAG? The 3 components and when not to use it

RAG has 3 moving parts: ingestion, retrieval, and generation. Here's what each does, when RAG beats fine-tuning, and when to skip it entirely.

Apr 22, 2026 · 6 min read glossaryragfundamentals

Context windows explained: why your 128k model only gives you 100k

The context window is your LLM's working memory per call. What 128k tokens actually fits, why usable size is smaller than advertised, and how to check yours.

Apr 21, 2026 · 6 min read glossaryfundamentalsvibe-coders