Prompt Craft

Coverage

• Role and instruction layering: how to compose a system prompt that scopes the model's behaviour without over-constraining
• Context insertion order: where to put the task, the constraints, the examples, and the input — and why the order matters more than the content
• Few-shot example selection: when 0-shot is enough, when 1-shot is sufficient, when 3-5 shots are necessary, and when 10+ shots are a sign the prompt is wrong
• Output-format constraints: how to get strict JSON, strict Markdown, strict plain text, and how to recover when the model breaks the format
• Negative instructions: when "DO NOT do X" works, when it backfires (the X-mention reinforcement effect), and the principle of replacing prohibitions with positive specifications
• Chain-of-thought and reasoning prompting: when explicit reasoning improves accuracy, when it introduces noise, and how to budget reasoning tokens
• Adversarial-input awareness: how user-controlled content can attempt to subvert system instructions, and the defence patterns (delimitation, output validation, allowlists)
• Iterative improvement: the prompt-eval loop — measure baseline, change one thing, measure delta, keep or discard
• Provider differences: where Claude, GPT, Gemini, and open-weight models differ in their response to the same prompt structure
• The "prompt as program" mental model: prompts as executable specifications with deterministic-ish inputs and probabilistic outputs

Philosophy

A prompt is a specification, not a wish list. A good prompt is the smallest set of instructions that produces the correct output reliably. Brevity is not the goal — necessity is. Every sentence in the prompt earns its place by visibly improving outputs in eval runs; sentences that survive without justification are noise that the model has to pattern-match through.