How to Use AI While Maintaining Quality

Stop asking AI tools like ChatGPT, Gemini, or Claude to edit and rewrite your marketing copy, emails, or other assets. Instead, use them as collaborative partners to help you improve the quality of your work. Here's how 👇 Ask your AI tool to review your work as the editor you want it to be. Are you looking for copy edits for grammar? Changes to stay on brand? Adaptation for a specific vertical? The perspective of your target persona? Give it specific guidance and the skills to be that exact editor. Then provide all of the appropriate context needed to do a great job. Share your goals, audience, brand guidelines, purpose, and/or whatever else a human would need to know to do a good job on the edits. Now comes the magic - request the AI review your copy for suggested changes. Ask it to give you three things for every edit it suggests:   - The original copy you wrote.   - Its suggested revisions. - The reasoning behind each change it suggested. This method works so much better than just asking the AI to re-write your copy and make it better because when you edit using my before/after/why framework you'll get... 1️⃣. Higher-quality edits When the AI is required to explain its suggestions, it avoids making changes just for the sake of making changes. This leads to more thoughtful, meaningful, high-quality improvements. 2️⃣. YOU stay connected Applying the AI’s suggestions yourself keeps you actively involved. You won’t accidentally become complacent (it's so easy with AI!) and blindly accept poor edits that degrade rather than enhance the quality of work. 3️⃣. Critical thinking helps a lot Understanding the reasoning behind a suggestion helps you decide if you agree with the logic. Even if you don’t love the execution, you can adopt the thinking behind the suggestion and adjust the execution to fit your voice and goals. 4️⃣ . The AI may catch edits you might overlook AI can flag things you didn’t notice, giving you the chance to refine them in your own way. This approach works especially well with tools like Gemini in Google Docs, Copilot in Word, or ChatGPT and Claude in a chatbot environment. While it might take a little longer to apply the suggestions, the payoff in quality is well worth it. You'll get higher-quality results and a deeper understanding of your own work. We talk a lot about AI efficiency gains, but AI isn’t just about saving time. One of the biggest reasons to build AI skills is because it improves the quality - not just the speed - of work. In fact, CMOs whose marketing teams I've trained with AI skills over the last 2 years frequently tell me post-training that they can really see who is actively using AI because of the dramatic increase in the quality of their work (and how much better it is than other people's now)! So if you've been asking ChatGPT to re-write your copy for you, try this method with your next project instead, and see how much better it is!

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

Today, let me share my two cents on AI Coding Assistants ... I have been using code assistants like Cursor and GitHub Copilot extensively recently. While productivity gains are undeniable, certain nuances must be considered to maintain long-term code quality. First, the notable advantages: >> Efficient Debugging and Documentation: AI assistants are excellent for generating unit tests, documentation, and brainstorming design patterns. Once I encountered a complex environment variable path conflict caused by multiple dependency versions. This type of issue is notoriously difficult to isolate, yet Cursor identified the root cause in under ten minutes. It saved hours of manual debugging. >> Rapid Prototyping: Exploring new frameworks is now straightforward. This provides leverage for researchers and non-engineers to build MVPs via "vibe coding" with ease. However, there are many pitfalls >> Code Verbosity: AI assistants, particularly Claude models, frequently generate more code than is strictly necessary. While some argue that prompt engineering can mitigate this, it remains difficult to prevent the AI from introducing over-complicated logic. >> Lack of Coherence: Automated changes can sometimes lack consistency across multiple files, likely due to internal context window limitations. Additionally, the tendency to include superfluous detail in documentation can clutter a codebase. >> Stale Training Data: LLM knowledge is often several months behind the latest releases. This is evident with fast-evolving libraries like TensorFlow. Relying on AI patches for outdated library versions without understanding the underlying mechanics significantly increases technical debt. Here are my recommendations for responsible usage >> Scrutinise Every Line: I would advise all developers, particularly those earlier in their careers, to avoid the temptation of "Tab-to-complete" without full comprehension. Challenge your AI assistant’s reasoning until you are satisfied. It may seem time-consuming initially, but it prevents costly architectural errors in the future. >> Transparency in Pull Requests: We should be honest about our AI usage. If more than 50% of a PR is AI-generated, it should ideally require two human peer reviewers. Furthermore, such code must be held to a higher standard regarding unit test coverage and quality scores. >> The Need for AI Audit Logs: There is a significant opportunity for IDEs to automate AI audit logs within PRs. These logs could specify the LLM used and the percentage of code generated versus refined. This would allow for better guardrails; for instance, code generated by one model could be cross-reviewed by another (such as Gemini or GPT) for an independent quality check. AI is a formidable tool but no substitute for critical thinking. To avoid technical debt, we must remain the primary architects of our systems. #SoftwareEngineering #AI #VibeCoding #CleanCode #TechLeadership

AI can generate information that sounds accurate but is completely wrong. AI hallucinations can undermine trust in reporting, introduce compliance exposure, and create financial or operational losses. They can also surface sensitive data or misinform decisions that affect capital allocation, investor communication, and audit readiness. AI hallucinations are not a signal to slow down innovation. They are a signal to strengthen your governance and controls. With a thoughtful risk management approach, leaders can understand uncertainty and build a more confident, resilient AI strategy. Considerations for leaders to reduce AI hallucination risk: 1. Create a validation and review process for AI generated financial outputs. Leaders must ensure that any AI generated forecasts, variance analyses, reconciliations, or narrative summaries have structured validation for source accuracy and logic. 2. Strengthen compliance and regulatory controls within AI workflows. AI hallucinations can create errors that lead to noncompliance and regulatory exposure. Leaders can embed compliance checkpoints into AI driven processes to avoid misstatements, inaccurate filings, or unintended disclosure. 3. Prioritize data governance using high quality, company specific data to reduce the risk of fabricated or inaccurate outputs. This is critical for forecasting, scenario modeling, and automated reporting. 4. Use retrieval augmented generation and automated reasoning for workflows. Pairing these methods anchors AI generated analysis in verified data sources rather than probability-based guesses. 5. Enable filtering and moderation tools to block misleading or irrelevant results. Teams cannot work from flawed or unverified outputs. Filters help prevent misleading content from entering critical workflows or influencing decisions. AI is gaining traction. Now is the time to formalize your AI risk mitigation approach. Start the discussion within your leadership team today. Identify where AI is already influencing decision-making, assess your current controls, and define the safeguards you need next. #RiskManagement #AI #Leaders

AI has made it easier than ever to produce content quickly, clearly, and at scale. The challenge, however, is that the same efficiency that improves output can also quietly dilute originality if it is not used intentionally. Over time, it becomes easy to rely on AI not just for structure or refinement, but for direction itself. When that happens, the work may appear polished and coherent, but it often begins to lose distinction. It sounds correct, but not necessarily personal. It communicates, but it does not always connect. Maintaining your voice in this environment requires a deliberate approach. It starts with ensuring that your thinking comes before the tool. When AI becomes the starting point, it shapes the narrative in ways that are subtle but significant. When your ideas lead, AI can support, refine, and expand them without replacing them. It is also important to use AI as a way to challenge your perspective rather than substitute it. Asking better questions, testing assumptions, and refining clarity can elevate your thinking, but the core ideas should still originate from you. Equally important is the editing process. While AI can improve clarity and structure, it cannot replicate your tone, your emphasis, or the nuances that make your communication recognizable. Preserving those elements requires intentional adjustment rather than blind acceptance of generated output. There is also value in knowing when to stop refining. Over-optimization often removes the very elements that make content feel human. Not everything needs to be perfectly structured to be effective, especially when authenticity is what creates connection. Ultimately, the goal is not just to produce more, but to produce work that remains distinctly yours. AI can enhance your output, but only if it is used in a way that supports your voice rather than replaces it. When you look at your recent work, does it sound more like you, or more like the tool you are using? #AI #ContentCreation #PersonalBrand #Creativity #FutureOfWork

If you’re not using AI everywhere you can, you’re missing an opportunity. But here’s the part people skip: AI is only as powerful as the data behind it. Great AI requires an even better data lead, governance, and intent. I use AI daily not as a novelty, but as an operating layer. AI is being embedded into every platform, workflow, and operating model, often faster than organizations are ready for. The question isn’t if AI will show up in your business; it’s whether you’re prepared to govern it, trust it, and extract real value from it. Without strong data leadership, clear ownership, and disciplined governance, AI simply accelerates noise, bias, and risk. With the right foundation, it becomes a force multiplier, improving speed, insight, and decision quality across the enterprise. On a personal level, AI helps me accelerate thinking, structure complex ideas, and improve decision quality. I use it to synthesize research, draft communications, and stress-test strategic options before I commit. Professionally, AI is embedded in how I work. I rely on large language models to analyze and summarize massive volumes of technical, regulatory, and business information turning unstructured data into clear, actionable insights. Most importantly, I don’t just consume AI - I build with it. I’ve been using AI since 2007, across insurance processes, predictive risk modeling, cybersecurity threat detection, and operational automation. Day to day, I use AI to prototype workflows, design agent-based architectures, define evaluation criteria, and continuously test outputs for accuracy, bias, and reliability. Beyond work, I use AI to learn and teach staying current on emerging research, experimenting with new models, and developing coursework that helps teams adopt AI responsibly and effectively. AI isn’t something I “check in on.” It’s how I think faster, build better systems, and make smarter decisions with strong human judgment, governance, and accountability at the center. #deepikachopra Order her book Move First Align Fast ! Cheers Deepika

How to Close the "Trust Gap" for AI in Medical Device Quality If you ask quality managers whether they trust AI for complaint handling, the answer is usually “not yet.” In this industry, a mistake is a missed patient safety signal that leads straight to an FDA 483. The barrier to adoption isn’t the software, it’s the fear of an unknown failure. To move from theory to reality, we must look at AI through the eyes of the people responsible for its performance. They need demonstrated confidence and consistency. If your AI gives a different answer to the same complaint today than it did last week, your validation is void. We have to stop treating AI as a black box and start managing it like controlled manufacturing process using Statistical Process Control (SPC). AI systems don’t fail dramatically, they drift. Four key areas of concern: ▪️ Concept Drift (Categorization): Your internal rules for “risk” change, but the AI is still running on old training logic. A Bernoulli CUSUM chart monitors misclassification rates and signals the exact moment your categorization logic goes out of control. ▪️ Data Drift (Semantic): Patients use new slang for a defect the AI doesn’t recognize. We convert text into high-dimensional embeddings and use Principal Component Analysis (PCA) to reduce the noise. By monitoring you can spot when the language has shifted enough to require a prompt update. ▪️ Model Surprisal (Confidence): The AI encounters an unusual complaint and "guesses" an answer. Tracking Perplexity and Log-probabilities measures how "surprised" the model is by its own output. A spike in surprisal triggers human review before the record is finalized. ▪️ Output Drift (Consistency): Silent updates from LLM providers can cause identical inputs to be categorized differently over time. Change Point Analysis (CPA) using the PELT algorithm identifies structural breaks in consistency that would otherwise stay hidden until an audit. The Solution: Agents Monitoring Agents The best way to ensure performance is a multi-agent supervisor architecture. You build a team with specific oversight roles: A Worker Agent handles the record. An Auditor Agent checks if the evidence actually supports the risk label. A Consistency Agent re-runs “Golden Set” cases to catch drift, while a Pattern Recognition Agent watches for broad trends. Each agent has one job, which makes the system auditable. Practical Actions for Regulatory Readiness Treat AI like a manufacturing line. Audit a fixed percentage of outputs. If disagreement rates between the Worker and Auditor climb, trigger a CAPA workflow immediately. Set model temperature to zero for deterministic outputs and document every prompt update in your QMS. If you can’t show an auditor exactly what changed, it didn’t happen. By moving to a supervised multi-agent ecosystem, you replace hope with process control. #AI #DigitalQuality #AdaptiveQualitySystems

Does Using AI Assistants Lead to Lower Code Quality? Recent research analyzing 150M+ lines of code reveals that while AI tools like GitHub Copilot speed up development, they also correlate with higher code churn, duplication, and lower maintainability. AI’s Limitations: Don’t: Assume AI code is flawless or context-aware. Do: Review and refine AI outputs. Project Context: Don’t: Let AI dictate your coding style and architecture. Do: Adapt AI suggestions to align with your team’s standards. Quality Metrics: Don’t: Ignore key indicators such as code churn and duplication. Do: Use rigorous testing, continuous integration, and analytics. Strategy & Oversight: Don’t: Rely solely on AI without a clear plan. Do: Combine AI’s speed (up to 55% faster, 46% more code) with strategic human review. Quality Over Quantity: Don’t: Prioritize rapid code generation at the cost of maintainability. Do: Focus on high-quality, well-refactored code. Keeping Code Up-to-Date: Don’t: Allow outdated AI outputs to persist. Do: Schedule regular reviews and refactoring sessions. Security & Performance: Don’t: Skip comprehensive testing and security reviews. Do: Conduct regular assessments and audits to catch vulnerabilities. Leverage AI for routine tasks (e.g., generating unit tests) but always pair it with human oversight to prevent technical debt. How do you integrate AI into your workflow? Share your insights below!

AI Integration and Safeguards How Intelligence Is Applied Responsibly Preo Communications integrates AI as an operational layer that enhances judgment, speed, and accuracy without introducing unnecessary risk. The objective is controlled leverage, not automation for its own sake. Where AI Is Applied AI is used in areas where it meaningfully improves outcomes. Common applications include: Pattern detection in analytics and attribution Forecasting and scenario modeling Audience segmentation and personalization Content optimization and performance analysis Workflow automation and efficiency gains AI supports teams by surfacing insight faster and reducing manual overhead. Human Led Decision Making AI informs decisions, it does not make them. Strategic direction, prioritization, and brand judgment remain human-led. AI outputs are treated as inputs to evaluation rather than instructions to follow without context. This prevents over-optimization and protects brand integrity. Data Quality and Input Control AI performance depends on data discipline. Inputs are carefully selected, cleaned, and structured to avoid bias, leakage, or misleading conclusions. Models are adjusted as data sources change to maintain reliability over time. Guardrails and Testing AI systems are introduced incrementally. Each application is tested in controlled environments before being expanded. Performance thresholds, review checkpoints, and rollback options are defined in advance to limit downside risk. Transparency and Traceability Outputs must be explainable. AI-driven insights are documented and traceable so teams understand why a recommendation exists and how it was generated. This maintains trust and supports better decision-making. Why AI Governance Matters Unstructured AI adoption increases volatility and risk. Governance ensures that efficiency gains do not come at the cost of accuracy, compliance, or strategic clarity. AI becomes valuable when it is embedded into well-designed systems with clear ownership and oversight. By applying AI deliberately and responsibly, Preo Communications enhances performance while preserving control, consistency, and long-term resilience.