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  "title": "Prompt Engineering: Instruction Design, Not Magic Words",
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  "thesis": "Prompt engineering is the disciplined design of instructions, examples, constraints, and evaluation criteria so that a language model produces useful, reliable output.",
  "status": "published",
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  "publishedAt": "2026-06-29",
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    "slug": "ai-demystified",
    "title": "AI, De-Mystified",
    "order": 4,
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  "claims": [
    {
      "id": "claim-001",
      "claim": "A prompt is not just a question; it is the designed instruction, context, examples, and constraints that shape what a language model produces.",
      "confidence": "high",
      "status": "core",
      "evidence": [
        {
          "sourceId": "source-brown-few-shot",
          "snippet": "Language models can perform a wide range of tasks from natural-language instructions and from a few examples concatenated in the model's context.",
          "supports": "direct",
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      "counterevidence": [
        {
          "summary": "For simple tasks, a bare question can be enough; prompt design becomes important mainly as tasks grow more specific or complex.",
          "assessedAt": "2026-06-29"
        }
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      "id": "claim-002",
      "claim": "Prompt engineering resembles older practices such as clear writing, task design, and human-computer interaction, updated for probabilistic language models.",
      "confidence": "high",
      "status": "landscape",
      "evidence": [
        {
          "sourceId": "source-openai-prompt-guide",
          "snippet": "Prompt engineering best practices include writing clear instructions, providing reference text, and splitting complex tasks into simpler subtasks.",
          "supports": "background",
          "assessedAt": "2026-06-29"
        }
      ],
      "counterevidence": [
        {
          "summary": "Unlike human readers, language models lack persistent memory, real-world grounding, and explicit intent, so analogies to human instruction have limits.",
          "assessedAt": "2026-06-29"
        }
      ]
    },
    {
      "id": "claim-003",
      "claim": "Practical prompt engineering uses techniques—such as giving examples, breaking tasks into steps, and defining output formats—to steer model behavior.",
      "confidence": "medium-high",
      "status": "design",
      "evidence": [
        {
          "sourceId": "source-brown-few-shot",
          "snippet": "Few-shot prompting provides input-output examples in the context, allowing the model to infer the desired pattern.",
          "supports": "direct",
          "assessedAt": "2026-06-29"
        },
        {
          "sourceId": "source-wei-cot",
          "snippet": "Chain-of-thought prompting elicits reasoning by prompting the model to generate intermediate reasoning steps before the final answer.",
          "supports": "direct",
          "assessedAt": "2026-06-29"
        },
        {
          "sourceId": "source-bsharat-instructions",
          "snippet": "Empirical studies of principled instructions find that specifying output format and asking the model to reason step by step improve task performance.",
          "supports": "direct",
          "assessedAt": "2026-06-29"
        }
      ],
      "counterevidence": [
        {
          "summary": "These techniques vary in effectiveness across models and tasks; some tasks are better addressed with fine-tuning, retrieval, or tool use than with prompting alone.",
          "assessedAt": "2026-06-29"
        }
      ]
    },
    {
      "id": "claim-004",
      "claim": "Prompt engineering is a powerful interface tool, but it cannot fix model errors, guarantee truthfulness, or replace evaluation and oversight.",
      "confidence": "medium",
      "status": "risk",
      "evidence": [
        {
          "sourceId": "source-openai-prompt-guide",
          "snippet": "Prompt engineering is iterative; outputs should be evaluated against ground truth because models can produce plausible-sounding but incorrect answers.",
          "supports": "indirect",
          "assessedAt": "2026-06-29"
        }
      ],
      "counterevidence": [
        {
          "summary": "In narrow, well-specified domains, carefully designed prompts can substantially reduce certain classes of errors, so the limits are context-dependent.",
          "assessedAt": "2026-06-29"
        }
      ]
    }
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      "id": "source-brown-few-shot",
      "title": "Brown et al.: Language Models are Few-Shot Learners",
      "url": "https://arxiv.org/abs/2005.14165",
      "type": "paper",
      "accessed": "2026-06-29"
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      "id": "source-wei-cot",
      "title": "Wei et al.: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models",
      "url": "https://arxiv.org/abs/2201.11903",
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      "accessed": "2026-06-29"
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      "id": "source-bsharat-instructions",
      "title": "Bsharat et al.: Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4",
      "url": "https://arxiv.org/abs/2312.16171",
      "type": "paper",
      "accessed": "2026-06-29"
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      "id": "source-openai-prompt-guide",
      "title": "OpenAI: Prompt Engineering",
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