llm-app-engineering

Context window management and token budgeting maintain high recall while respecting model limits. This skill applies structured patterns to automatically balance token usage across prompt chains, ensuring your LLM applications stay within operational constraints without losing critical input data.

Enforcing structured outputs requires schema-guided prompts and few-shot engineering patterns. This skill automates these patterns to guarantee deterministic, safe LLM behavior, ensuring responses integrate seamlessly with downstream systems and maintain reliable formatting across your application workflows.

Automated evaluation pipelines catch regressions and enforce prompt safety by running systematic checks. This skill helps you design and implement these eval pipelines to test prompt chains, evaluate outputs, and ensure your production-grade prompts perform reliably over time.

Streaming responses improve user experience by delivering LLM outputs progressively. This skill applies engineering patterns to implement streaming alongside structured outputs, enabling real-time interaction and smoother integration with downstream systems in full-stack LLM applications.

Inconsistent LLM outputs often result from unmanaged context, unsanitized inputs, or lack of evaluation. This skill enforces input sanitization, token budgeting, and automated eval pipelines to eliminate regressions and ensure deterministic, safe LLM behavior in production environments.

Automated evaluation pipelines are essential for any LLM application requiring deterministic behavior. Even small use cases benefit from regression checks and safety practices to catch prompt failures early, ensuring reliable outputs as your application scales and prompts evolve.