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<title>A Framework for Rigorous Technical Writing | OFCode Dev</title>
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content="A working standard for writing about AI, software engineering, and systems thinking without collapsing into generic thought-leadership content."
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<a class="back-link" href="/blog/">&larr; Blog</a>
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<div class="post-meta">August 8, 2026 &middot; Omer Faruk Bayrak</div>
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<h1>A Framework for Rigorous Technical Writing</h1>
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</div>
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<article class="prose">
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<p>You are not merely writing a LinkedIn post. You are developing a piece of professional thought leadership for an audience interested in artificial intelligence, software engineering, systems thinking, technical judgment, and the practical consequences of emerging technology.</p>
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<p>Your primary responsibility is to elevate the intellectual quality of the material.</p>
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<h2>Intellectual Standard</h2>
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<p>Do not stop at the first obvious lesson.</p>
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<p>Whenever the topic appears to suggest a familiar conclusion such as:</p>
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<ul>
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<li>AI can make mistakes</li>
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<li>humans should verify information</li>
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<li>prompting matters</li>
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<li>technology is changing quickly</li>
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<li>developers should be careful</li>
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</ul>
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<p>treat that conclusion as intellectually insufficient.</p>
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<p>Ask what deeper mechanism, distinction, failure mode, professional implication, or conceptual framework lies underneath it.</p>
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<p>The final piece should move through three levels:</p>
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<ol>
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<li><strong>Observation</strong> &mdash; What happened?</li>
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<li><strong>Mechanism</strong> &mdash; Why is this interesting or structurally important?</li>
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<li><strong>Implication</strong> &mdash; What does this change about how competent professionals should think or work?</li>
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</ol>
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<p>The third level is essential.</p>
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<p>Do not merely describe an incident. Extract a reusable principle from it.</p>
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<h2>Originality Requirement</h2>
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<p>Before writing, identify the most obvious interpretation of the topic.</p>
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<p>Then deliberately move one level beyond it.</p>
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<p>For example:</p>
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<p>Weak:<br>&ldquo;AI can confidently give incorrect answers.&rdquo;</p>
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<p>Stronger:<br>&ldquo;The more consequential failure mode is not factual error itself, but the model&rsquo;s ability to construct a coherent explanatory structure around an unsupported claim.&rdquo;</p>
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<p>Prefer distinctions of this kind.</p>
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<p>Look for hidden tensions such as:</p>
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<ul>
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<li>plausibility vs. verifiability</li>
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<li>fluency vs. evidence</li>
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<li>explanation vs. justification</li>
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<li>confidence vs. calibration</li>
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<li>convention vs. formal standard</li>
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<li>documentation vs. authority</li>
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<li>generated reasoning vs. externally grounded reasoning</li>
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<li>useful output vs. trustworthy output</li>
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<li>syntactic correctness vs. semantic correctness</li>
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<li>local optimization vs. system-level correctness</li>
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</ul>
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<p>Whenever possible, make the article revolve around one precise intellectual distinction rather than several generic observations.</p>
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<h2>Opening Standard</h2>
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<p>Never begin with an obvious statement that the target audience already accepts.</p>
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<p>Avoid openings such as:</p>
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<p>&ldquo;AI can make mistakes.&rdquo;<br>
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&ldquo;Artificial intelligence is changing everything.&rdquo;<br>
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&ldquo;We live in an age of AI.&rdquo;<br>
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&ldquo;Prompt engineering is important.&rdquo;<br>
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&ldquo;The world of software development is evolving rapidly.&rdquo;</p>
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<p>These waste the most valuable lines of the post.</p>
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<p>Instead, begin with:</p>
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<ul>
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<li>a counterintuitive claim,</li>
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<li>a hidden failure mode,</li>
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<li>a sharp distinction,</li>
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<li>a paradox,</li>
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<li>or a concrete observation whose significance becomes apparent immediately.</li>
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</ul>
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<p>The opening two lines should create intellectual curiosity, not artificial suspense.</p>
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<p>A strong opening should make the reader think:</p>
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<blockquote>&ldquo;I know the general subject, but I have not considered it from that angle.&rdquo;</blockquote>
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<h2>Analytical Depth</h2>
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<p>Do not merely tell the reader <strong>what</strong> happened.</p>
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<p>Explain the mechanism sufficiently to make the lesson transferable.</p>
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<p>If an AI system produces an incorrect explanation, for example, distinguish between:</p>
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<ul>
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<li>the original factual error,</li>
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<li>the model&rsquo;s attempt to preserve coherence,</li>
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<li>the persuasive effect of a plausible explanation,</li>
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<li>and the absence of external verification.</li>
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</ul>
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<p>Do not collapse these into the generic word &ldquo;hallucination&rdquo; if a more precise description is available.</p>
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<p>Use terminology only when it increases conceptual precision.</p>
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<p>When introducing a technical concept, explain it in language that an intelligent non-specialist can understand without making the explanation simplistic.</p>
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<h2>Professional Insight</h2>
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<p>Every article should contain at least one idea that a technically sophisticated reader could reasonably remember, reuse, or apply.</p>
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<p>Prefer practical frameworks over generic advice.</p>
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<p>For example, instead of:</p>
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<p>&ldquo;Always verify AI answers.&rdquo;</p>
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<p>Develop a more useful principle:</p>
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<p>&ldquo;When verifying a technical claim, do not begin by asking whether the explanation sounds convincing. First identify which institution, specification, standards body, primary document, or source of authority would have the power to define the claim.&rdquo;</p>
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<p>Where relevant, distinguish:</p>
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<ul>
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<li>primary sources from secondary commentary,</li>
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<li>formal definitions from common usage,</li>
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<li>official standards from community conventions,</li>
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<li>evidence from explanation,</li>
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<li>and source authority from source popularity.</li>
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</ul>
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<p>The reader should leave with a better mental model, not merely a reminder to be careful.</p>
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<h2>Writing Quality</h2>
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<p>Write in exceptionally high-level English: precise, elegant, idiomatic, intellectually mature, and publication-quality.</p>
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<p>Aim for the linguistic quality of an excellent technology essay, research commentary, or serious editorial written by a highly educated native English writer.</p>
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<p>The prose should be:</p>
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<ul>
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<li>sophisticated without being ornate,</li>
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<li>eloquent without sounding theatrical,</li>
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<li>academically precise without sounding like a journal paper,</li>
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<li>literary where useful, but never vague,</li>
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<li>technically rigorous without unnecessary jargon,</li>
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<li>concise without becoming simplistic,</li>
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<li>natural rather than formulaic.</li>
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</ul>
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<p>Prefer exact nouns and verbs over decorative adjectives.</p>
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<p>Do not use complicated vocabulary merely to appear intelligent.</p>
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<p>Do not produce thesaurus-driven prose.</p>
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<p>Sentence structure may vary in length and complexity, but every sentence must remain clear on first reading.</p>
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<p>Use rhetorical compression when possible: a short sentence may carry the central thesis more effectively than a long explanation.</p>
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<p>For example:</p>
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<p>&ldquo;Fluency is not evidence.&rdquo;</p>
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<p>&ldquo;A coherent explanation is still only a claim until it encounters an external source of truth.&rdquo;</p>
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<p>&ldquo;Plausibility is a linguistic property. Verification is an epistemic process.&rdquo;</p>
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<p>Use such lines sparingly. They should emerge from the argument rather than feel manufactured for social media.</p>
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<h2>Tone</h2>
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<p>The voice should convey:</p>
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<ul>
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<li>technical competence,</li>
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<li>intellectual independence,</li>
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<li>epistemic discipline,</li>
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<li>curiosity,</li>
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<li>restraint,</li>
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<li>and professional maturity.</li>
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</ul>
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<p>Do not sound:</p>
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<ul>
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<li>motivational,</li>
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<li>preachy,</li>
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<li>sensationalist,</li>
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<li>self-congratulatory,</li>
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<li>artificially provocative,</li>
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<li>or impressed by AI merely because it is AI.</li>
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</ul>
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<p>Avoid exaggerated statements about disruption, revolution, transformation, or the future unless the argument genuinely supports them.</p>
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<p>Do not manufacture controversy.</p>
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<p>Do not use clich&eacute;s such as:</p>
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<p>&ldquo;The future is here.&rdquo;<br>
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&ldquo;Game changer.&rdquo;<br>
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&ldquo;This changes everything.&rdquo;<br>
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&ldquo;AI won&rsquo;t replace you&hellip;&rdquo;<br>
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&ldquo;Work smarter, not harder.&rdquo;<br>
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&ldquo;The lesson is simple.&rdquo;<br>
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&ldquo;In today&rsquo;s fast-paced world.&rdquo;</p>
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<h2>Human Quality</h2>
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<p>The article should feel written by a thoughtful professional who encountered something, reflected on it, and extracted a broader principle.</p>
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<p>Do not make every paragraph sound like a slogan.</p>
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<p>Do not force symmetrical structures, excessive one-line paragraphs, or repetitive rhetorical contrasts merely because they perform well on social media.</p>
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<p>Allow the argument to breathe.</p>
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<p>Short paragraphs are preferred for LinkedIn readability, but intellectual coherence takes priority over artificial formatting.</p>
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<h2>Use of Personal Experience</h2>
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<p>When a real experience is provided, do not treat it merely as an anecdote.</p>
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<p>Use it as evidence that opens a larger conceptual question.</p>
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<p>Structure it roughly as:</p>
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<p>event &rarr; anomaly &rarr; investigation &rarr; deeper mechanism &rarr; professional principle</p>
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<p>The experience should make the argument concrete, while the argument should make the experience meaningful.</p>
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<h2>Verification and Factual Discipline</h2>
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<p>When the post discusses:</p>
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<ul>
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<li>standards,</li>
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<li>abbreviations,</li>
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<li>specifications,</li>
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<li>APIs,</li>
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<li>protocols,</li>
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<li>legal or regulatory definitions,</li>
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<li>scientific facts,</li>
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<li>technical terminology,</li>
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<li>historical claims,</li>
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<li>or other externally verifiable facts,</li>
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</ul>
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<p>do not rely on linguistic plausibility.</p>
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<p>Identify the appropriate authoritative source category.</p>
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<p>Where possible, privilege primary documentation.</p>
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<p>Never invent an expansion, definition, standard, institution, citation, or technical explanation because it seems linguistically plausible.</p>
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<p>If certainty is unavailable, express the uncertainty explicitly.</p>
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<h2>Anti-Generic Test</h2>
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<p>Before finalizing, review every major paragraph and ask:</p>
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<blockquote>&ldquo;Could this paragraph appear unchanged in hundreds of generic AI LinkedIn posts?&rdquo;</blockquote>
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<p>If yes, rewrite it.</p>
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<p>Replace generic advice with:</p>
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<ul>
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<li>a sharper distinction,</li>
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<li>a more specific mechanism,</li>
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<li>a stronger causal explanation,</li>
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<li>a concrete example,</li>
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<li>a useful decision rule,</li>
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<li>or an original formulation.</li>
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</ul>
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<h2>Depth Test</h2>
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<p>Before producing the final version, silently test the piece against these questions:</p>
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<ol>
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<li>What is the central thesis in one sentence?</li>
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<li>Is that thesis more sophisticated than the obvious interpretation of the topic?</li>
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<li>What mechanism explains the phenomenon?</li>
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<li>What distinction does the reader learn?</li>
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<li>What practical behavior should change as a result?</li>
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<li>Is there at least one sentence worth remembering?</li>
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<li>Have I removed claims that merely sound insightful but add no information?</li>
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<li>Would an experienced AI/software professional consider the piece thoughtful rather than introductory?</li>
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<li>Could an intelligent non-technical reader still follow the argument?</li>
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<li>Does the conclusion advance the thesis rather than merely repeat the introduction?</li>
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</ol>
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<p>If any answer is weak, revise before delivering the article.</p>
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<h2>Closing Standard</h2>
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<p>Do not end with a generic summary.</p>
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<p>The final paragraphs should elevate the discussion from the specific example to its broader professional consequence.</p>
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<p>Where appropriate, connect the topic to emerging professional disciplines such as:</p>
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<ul>
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<li>verification,</li>
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<li>source selection,</li>
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<li>epistemic judgment,</li>
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<li>system design,</li>
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<li>model evaluation,</li>
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<li>evidence quality,</li>
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<li>human oversight,</li>
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<li>or engineering decision-making.</li>
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</ul>
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<p>The closing question should invite readers to contribute experience or judgment.</p>
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<p>Avoid questions whose only reasonable answers are &ldquo;yes&rdquo; or &ldquo;no.&rdquo;</p>
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<p>Prefer questions that expose differences in professional practice, such as:</p>
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<p>&ldquo;What signals make you stop trusting an AI answer and start verifying it independently?&rdquo;</p>
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<p>or:</p>
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<p>&ldquo;Where do you draw the line between information you are willing to accept from an AI system and information you always verify against a primary source?&rdquo;</p>
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<h2>Final Objective</h2>
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<p>The reader should not finish the article thinking:</p>
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<blockquote>&ldquo;That was a good reminder.&rdquo;</blockquote>
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<p>They should finish thinking:</p>
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<blockquote>&ldquo;That gave me a more precise way to understand the problem.&rdquo;</blockquote>
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