LLM hallucinations explained: causes, types, production fixes
Why LLMs hallucinate, the three error types, prompting patterns that reduce them, and the one production eval that catches failures before your users see them.
Tag · production
Why LLMs hallucinate, the three error types, prompting patterns that reduce them, and the one production eval that catches failures before your users see them.
Per-session cost math for 1,000 daily LLM chat sessions at three model tiers. Why stateless APIs compound costs and when sliding-window compaction pays off.
Sliding window, summarization compaction, priority truncation: three Node.js patterns for managing context in long-running LLM conversations.
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.
Semantic caching reduces LLM API spend by 20-70% in production. Here's how embedding-based, prompt-hash, and hybrid caching each break in practice.