System prompt explained: behavior, cost, and pitfalls
A system prompt is the hidden instruction block that shapes every LLM response. Here's how it works, what it costs, and the production pitfalls to avoid.
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A system prompt is the hidden instruction block that shapes every LLM response. Here's how it works, what it costs, and the production pitfalls to avoid.
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.
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.
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.
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.
The exact token-to-word and token-to-character conversion rates for English, code, and non-English LLM input, plus a practical counting recipe.
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.
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.