Posts tagged “engineering”

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.

Resources represent data or files that an MCP client can read. A case study of the SQLite MCP server shows how resources and tools work together.

How to design tool responses that preserve context space for what matters. Filter early, return minimal data, and structure outputs for LLM consumption.

Five prompting techniques that improve LLM outputs: few-shot learning, chain-of-thought reasoning, XML structure, output constraints, and prompt chaining.

When models fail or behave unexpectedly, you need to understand why. Practical debugging techniques for tokenization, attention patterns, and context limits.