Key takeaways

Portable prompts beat vendor-specific magic strings: clear role, constraints, output schema, and examples. Prefer structured outputs over prose (/en/articles/china-llm-json-structured-output-2026). Keep a frozen eval set when you change models (/en/articles/china-llm-benchmarks-guide-2026).

Patterns that help

Bilingual system prompts for CN/EN products. Explicit refuse/escalation rules. Tool-calling schemas tested per vendor (/en/articles/china-llm-agent-tool-calling-2026). Long-doc prompts: chunk or use Kimi paths (/en/articles/china-llm-rag-selection-guide).

Cost-aware prompting

Shorten context, cap max_tokens, cache stable system text, route easy tasks to smaller models (/en/articles/china-llm-optimization-guide-2026).

Next steps on Swift Horse

Try /en/prompts → SDK /en/articles/china-llm-openai-compatible-sdk-2026 → LangChain /en/articles/china-llm-langchain-integration-2026.

FAQ

Do Chinese LLMs need different prompts than GPT?

Core patterns are similar; always re-validate schemas and bilingual edge cases per vendor.

Should prompts be Chinese or English?

Match the task language; many teams keep system bilingual for CN↔EN products.

Is prompt engineering enough vs fine-tuning?

Often yes early—see /en/articles/china-llm-fine-tuning-vs-api-2026.

Is this official prompt documentation?

No—independent Swift Horse engineering guide.