Editorial Style Guide

Proof to Production is a blog dedicated to cutting through AI hype and providing businesses and knowledge workers with practical, timely advice on navigating the AI transition. Our mission is to help organisations survive and thrive during this period of unprecedented technological change by focusing on what works in practice rather than theoretical possibilities.

Our readers are forward-looking knowledge workers, business leaders, and organisations committed to staying competitive as AI technologies transform their industries. They seek evidence-based insights, practical implementation guidance, and clear analysis of emerging trends - all delivered without the sensationalism that characterises much AI coverage.

This style guide outlines the editorial approach that defines Proof to Production and, we hope, distinguishes it from many other business technology publications.

Core Writing Principles


1. Rationalist, Evidence-Based Approach


Our writing reflects a commitment to rationalist discourse - examining AI developments through careful reasoning and evidence rather than hype or fear. This approach requires:


  • Grounding claims in observable evidence whenever possible

  • Acknowledging the limits of current knowledge

  • Reasoning transparently so readers can evaluate arguments independently


Instead of: "AI is clearly going to eliminate all knowledge worker jobs within five years."


Write: "Current adoption patterns suggest certain knowledge work tasks are being augmented or replaced by AI, though the extent and timeline of this transition remains uncertain and will likely vary significantly by domain."


2. Distinguishing Between Types of Knowledge Claims


Proof to Production content explicitly differentiates between three types of knowledge:


Known-Knowns


Claims that are established facts, directly observable, or logically necessary. These should be presented confidently but with appropriate evidence.


Instead of: "Everyone knows large language models have achieved human-level reasoning."


Write: "Recent benchmark results demonstrate that frontier language models can solve certain reasoning tasks at performance levels comparable to average human performance on the same tests, though significant limitations remain in areas such as planning and causal reasoning."


Known-Unknowns


Areas where we understand the question but lack definitive answers. These require qualified language and balanced presentation of competing hypotheses.


Instead of: "No one knows if AI models will continue scaling according to current trends."


Write: "While the scaling behaviour of transformer-based models has demonstrated remarkably consistent patterns in recent years, several factors could potentially disrupt this trajectory: availability of high-quality training data, computational constraints, diminishing returns at larger scales, or fundamental limitations in the architecture itself. Current evidence doesn't definitively resolve this uncertainty."


Unknown-Unknowns


Speculative areas where we may not even know what questions to ask. These require clear labelling as speculation and avoiding unwarranted confidence.


Instead of: "The impact of AI on society will undoubtedly be positive overall."


Write: "The societal impact of advanced AI capabilities remains highly uncertain. Historical technological revolutions suggest a complex mixture of benefits and disruptions, though the unique characteristics of AI - particularly its ability to automate cognitive rather than just physical tasks - make simple historical analogies insufficient for prediction."


3. Cutting Through Hype


Proof to Production exists to provide clarity amidst exaggerated claims about AI. Our content should:


  • Challenge oversimplified narratives about AI capabilities

  • Provide realistic assessments of current limitations

  • Focus on practical application rather than theoretical possibilities


Instead of: "Generative AI is revolutionising every industry overnight."


Write: "Organisations in specific sectors - particularly those involving content creation, software development, customer service, and certain types of data analysis - are finding substantive applications for generative AI, though implementation challenges and capability limitations mean adoption proceeds at different rates across industries."

Language & Tone


Clear, Precise Language


Use specific, precise language that conveys exactly what you mean without unnecessary qualification.


Instead of: "AI might potentially be somewhat useful for certain business applications in some contexts."

Write: "AI demonstrates particular utility in applications requiring pattern recognition in large datasets, natural language processing, and repetitive decision-making tasks."


Technical Terminology


While our audience consists primarily of knowledge workers and business leaders rather than AI researchers, we don't avoid technical concepts when necessary. Instead:


  • Define technical terms on first use

  • Explain concepts through concrete examples

  • Avoid unnecessary jargon when simpler language would suffice


Instead of: "The architecture's attention mechanism facilitates superior token prediction in the decoder layers."


Write: "The system's ability to focus on relevant information in context (its 'attention mechanism') enables it to predict what comes next in a text more accurately than previous approaches."


Avoiding Clichés and Generated-Text Patterns


Proof to Production content avoids linguistic patterns common in low-quality AI writing:


Instead of: "In today's fast-paced digital landscape, artificial intelligence is revolutionising the way businesses operate, driving innovation and unlocking unprecedented opportunities for growth and transformation."


Write: "AI tools are changing how certain business functions operate, with measurable impacts on productivity in specific domains."


British English


British contributors to Proof to Production should use British English spelling and conventions throughout. This includes:


  • Spelling: "organisation" not "organization", "colour" not "color"

  • Punctuation: single quotation marks for quotes within text

  • Terminology: "full stop" not "period", "brackets" not "parentheses"

  • Date format: day/month/year (15 April 2025)


American contributors may use American English spelling and conventions, although consistency should be maintained throughout.

Structure & Content Organisation


Practical Focus


Every article should contain actionable insights or practical analysis. Theory should lead to application.

Instead of: "The philosophical implications of language models raise interesting questions about cognition."

Write: "Understanding how language models process information helps organisations identify both their capabilities and limitations in practical applications like customer service automation."


Evidence and Examples


Support claims with:


  • Data and research where available

  • Documented case studies

  • Historical parallels (with appropriate caveats)

  • Logical reasoning


Instead of: "Most companies are struggling to implement AI effectively."


Write: "Recent survey data from [source] indicates that [x]% of mid-sized enterprises report significant implementation challenges with their initial AI projects, particularly in three areas: data quality issues, integration with existing systems, and measuring return on investment."


Balanced Perspective


Present multiple viewpoints on contested issues while still providing clear guidance where evidence permits.


Instead of: "The only viable approach is a fully centralised AI governance model."


Write: "Organisations are experimenting with various AI governance structures. Centralised models offer advantages in consistency and risk management, while decentralised approaches may enable faster adoption and domain-specific optimisation. The evidence suggests hybrid models work best for many enterprises, with centralised policy and decentralised implementation."

Specific Guidance for Future Speculation


When discussing the future development of AI, strike a balance between providing valuable foresight and acknowledging inherent uncertainties.


Grounding Speculation


Always base future-oriented claims on:


  • Established technological trends with clear supporting evidence

  • Known research directions and their logical extensions

  • Historical patterns from analogous technological developments



Instead of: "AI will obviously create more jobs than it eliminates."


Write: "Historical technological transitions suggest complex labour market effects. While automation has typically created new job categories over time, the transition period often involves significant displacement in specific sectors. AI's impact may follow similar patterns, though its ability to affect cognitive work distinguishes it from previous technologies in important ways."


Timeframes and Probabilities


When discussing future developments:


  • Specify timeframes where possible

  • Indicate degrees of confidence

  • Acknowledge alternative possibilities


Instead of: "Artificial general intelligence is coming soon."


Write: "Research teams are making steady progress on capabilities that contribute to more general AI systems, though significant technical challenges remain unsolved. Surveys of leading AI researchers show a wide distribution of timelines for achieving systems that match human capabilities across most domains, with substantial disagreement about both the pathway and timeline."


Labelling Speculation


Clearly distinguish between different levels of confidence in future projections:


Instead of: "The next generation of AI models will achieve reasoning capabilities comparable to domain experts."


Write: "While current trends in scaling and architectural improvements suggest continued progress in reasoning capabilities, it remains speculative whether approaching models will match domain expert performance across fields like law or medicine. Some researchers argue for fundamental limitations in current approaches, while others point to steady improvements on benchmark tasks as evidence for continued progress."

Article Structure Guidelines


Effective Proof to Production articles typically include:


  1. Clear premise – State the core insight or argument upfront

  2. Context – Provide sufficient background for business readers

  3. Evidence – Present supporting data, examples, and reasoning

  4. Implications – Explain practical significance for organisations

  5. Recommendations – Offer actionable guidance where appropriate

  6. Limitations – Acknowledge constraints and caveats


Final Checklist


Before submitting content, review for:


[ ] Precision in language and claims

[ ] Clear distinction between facts, reasoned extrapolations, and speculation

[ ] Definition of technical terms

[ ] Avoidance of clichés and generic business/tech phrases

[ ] Practical implications for readers

[ ] Appropriate evidence for claims

[ ] British English spelling and conventions

[ ] Narrative flow and readability