Your friendly neighborhood AI engineer

LLM responses arrive as chunks, not all at once. Handle loading, streaming, completion, and errors without breaking the user experience.

AI systems stream responses with variable length and timing. Here's how to design interfaces that show progress immediately and handle uncertainty gracefully.

Structure prompts to maximize Anthropic's prompt caching, reducing costs by 90% and latency by 85% for repeated context.

How to test LLM outputs with code-based grading, human evaluation, and LLM-as-judge. When to use each method and why statistical rigor matters.

Error messages consume context and affect LLM decision-making. Structure errors as data, use reference IDs for details, and return actionable recovery paths.