Claude web and mobile apps prepend a system prompt that’s periodically updated to shape behavior (including Markdown code formatting). Those updates don’t apply to the Claude API, so API apps must supply their own system instructions and pin model IDs for consistent results.
LLMs can generate useful text quickly, but domain knowledge is what turns that text into correct engineering or math. Expert prompting works by supplying constraints, verification signals, and diagnostic iteration—not by asking for “the right explanation.”
Research using simulated lifetimes finds that LLM financial advice can align with life-cycle planning—saving buffers, diversified equity exposure, and age-appropriate risk—especially when prompts include structured personal details and explicit assumptions. Weak spots appear around unemployment shocks and active portfolio rebalancing, and outcomes vary systematically with how prompts are framed.
Claude Code can embed hidden classification markers inside a “Today’s date…” system prompt by swapping Unicode apostrophes and changing date separators. The logic activates via ANTHROPIC_BASE_URL, timezone, and hostname matching against decoded lists.