Prompt engineering for coding is a systematic skill — not just "be specific"¶
Insight: The quality of AI coding output depends almost entirely on prompt quality — and effective prompting follows repeatable frameworks, not intuition. Three foundational principles: (1) Provide rich context — assume the AI knows nothing about your project, include language/framework/libraries/error messages, (2) Be specific about goals — "Why isn't my code working?" fails; a debugging formula like "Expected [X], getting [Y] with [input], where is the bug?" succeeds, (3) Break down complex tasks — iterate through smaller chunks rather than one massive prompt.
Detail: Osmani (former Chrome engineering leader, 100K+ X followers) frames prompt engineering as analogous to writing good specifications — the skills that make someone good at technical communication also make them good at prompting. The article includes side-by-side good vs. bad prompt comparisons with actual AI responses. This corroborates the "skill issue" framing from Shrivu Shankar's earlier article — bad AI output is primarily a prompting problem, not a model problem.