Prompt Engineering for LLM Pipelines

Core Philosophy: Bridging the Two Gulfs

Effective prompt engineering is fundamentally about closing two gaps between human intent and model behavior. Understanding which gap you're dealing with determines whether prompt refinement will actually solve your problem.

The Gulf of Specification (Developer → LLM)

This gulf separates what you mean from what you actually wrote in the prompt. Your intent — the task you want the LLM to perform — is often only loosely captured by the words you write. Specifying tasks precisely in natural language is surprisingly hard.

Even prompts that seem clear often leave crucial details unstated. For example:

"Extract the sender's name and summarize the key requests in this email."

This sounds specific, but critical questions are left unanswered:

• Should the summary be a paragraph or a bulleted list?
• Should the sender be the display name, the full email address, or both?
• Should the summary include implicit requests, or only explicit ones?
• How concise or detailed should the summary be?