llm-integration-patterns

Multi-tier LLM orchestration balances cost and quality by routing user-facing chat to premium models and background tasks to standard models, ensuring predictable expenses and reliable performance.

Structured prompt engineering with resilient response parsing and fallback safeguards handles LLM failures by applying tiered model selection and error handling to maintain application reliability.

Comprehensive activity logging tracks all LLM calls by recording model details and AUD estimates, providing complete auditing of API usage to keep costs predictable across modules.

Yes, a tiered LLM factory supports configuring model selection by assigning premium models to user-facing interactions and standard models to background tasks, optimizing both cost and performance.

Prompt engineering patterns manage LLM integration costs predictably by combining structured prompts with tiered model factories, resilient parsing, and comprehensive activity auditing.