Crafting Technical Documentation

Working with multiple LLM providers, prompt engineering, and complex data flows requires thoughtful organization. A proper structure helps teams: - Maintain clean separation between configuration and code - Implement consistent error handling and rate limiting - Enable rapid experimentation while preserving reproducibility - Facilitate collaboration across ML engineers and developers The modular approach shown here separates model clients, prompt engineering, utils, and handlers while maintaining a coherent flow. This organization has saved many people countless hours in debugging and onboarding. Key Components That Drive Success Beyond folders, the real innovation lies in how components interact: - Centralized configuration through YAML - Dedicated prompt engineering module with templating and few-shot capabilities - Properly sandboxed model clients with standardized interfaces - Comprehensive caching, logging, and rate limiting Whether you're building RAG applications, fine-tuning foundation models, or creating agent-based systems, this structure provides a solid foundation to build upon. What project structure approaches have you found effective for your generative AI projects? I'd love to hear your experiences.

One of the good coding practices that I picked up while working at Google is about writing helpful error messages. Instead of simply stating the error, almost always, the error message also contains steps to resolve it, links to refer to, a quick shortcut to post questions to the internal question-answering platform, or at least the direction to look into. This practice can be seen across all Google Products. Try integrating with YouTube APIs, and you will see errors that are not only verbose but helpful. This can also be seen for all GCP interactions, be it web console or CLI, where the error message contains a link to a document on how to fix the issue. I have personally started using this practice every time I write code. #AsliEngineering

𝗖𝗹𝗲𝗮𝗿 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗿𝗶𝘁𝗶𝗻𝗴. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. I came across a video about why clear documentation matters, and although it was lighthearted, it captured a challenge I've seen repeatedly while working with organizations. Most teams believe they have documented their processes. What they've often documented are the steps, while leaving out the judgment behind them. Experienced people fill in those gaps automatically because they know which shortcuts to take, which exceptions to ignore, and which decisions require extra attention. New colleagues don't have that advantage, which is why the same document that looks perfectly clear to one person can be frustratingly incomplete to another. 𝗜𝗳 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗼𝗻𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗼𝗼𝗺, 𝗶𝘁 𝗶𝘀𝗻'𝘁 𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. 𝗜𝘁'𝘀 𝘁𝗿𝗶𝗯𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲. The strongest organizations don't become resilient because they hire exceptional people. They become resilient because exceptional people make their knowledge transferable. They create documentation that explains not only what to do, but also why it matters, when to adapt, and how success should be measured. That's becoming an increasingly valuable leadership skill because every new hire, every cross-functional project, and every technology initiative depends on the same foundation. Clarity. 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝘀𝘁𝗶𝗹𝗹 𝗿𝗲𝗹𝘆 𝗼𝗻 𝘁𝗿𝗶𝗯𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗰𝗹𝗲𝗮𝗿 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻? #Leadership #KnowledgeManagement #FutureOfWork #Management #Communication Source: Noelidavid

Google's official agent skills repo is live and growing! 🤖☁️ As we build more with agentic AI, feeding agents accurate, up-to-date documentation is critical. But dumping massive doc sites into an agent's context window leads to "context bloat" model confusion, and high token costs. We wanted to make this easier for folks building on Google Cloud. Enter Google's agent skills: https://lnkd.in/gPw-uGkA Compact, agent-first documentation written in Markdown. Skills allow your AI agents to load reference files, code snippets, and specific expertise only as needed. The repo is under active development and packed with resources to level up your agents, including: 🔹 Agent Platform APIs: Gemini API, Gemini Interactions API, Managed Agents API, and Skill Registry API. 🔹 GCP Basics: AlloyDB, BigQuery, Cloud Run, Cloud SQL, Firebase, and GKE. 🔹 Well-Architected framework: Security, Reliability, Cost Optimization, Operational Excellence, Performance Optimization, and Sustainability. 🔹 Recipes: Google Cloud Onboarding, Authentication, and Network Observability. 💻 Ready to try it? You can easily select and install specific skills to your agents using: npx skills add google/skills We want your feedback! This repo is fully open-source. We'd love for the community to contribute by reporting bugs, suggesting new tech skills, or filing feature requests on GitHub. #ai #programming #softwareengineering

🪂 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.

How I maintain clarity in my code even with 20+ contributors: Do you find yourself staring at your own code, asking "Who wrote this terrible mess?" Even though you know it was you? You’re not alone. Poor documentation is a real headache even for a senior dev. With these 5 steps, you can avoid wasting hours trying to understand what your code means: 1. Create an Overview: Start with the project's purpose. This helps everyone stay on the same page from the beginning. 2. Detail Your Process: Break down your code step-by-step. This way, you won’t waste time rediscovering your own logic. 3. Include Visuals: Use charts or screenshots to illustrate key parts of your process.  A picture is worth a thousand lines of code!     4. Highlight Challenges and Solutions: Share what problems you faced and how you solved them. This not only showcases your problem-solving skills but also builds trust with your team. 5. Summarize Results:     Focus on outcomes and insights for business stakeholders, while also pointing out areas for further improvement for your fellow developers. Structure it like this: Introduction > Objectives > Methods > Results > Conclusion > Future Work. Clarity is key. Remember, understandable code is valuable code. Repost if you can relate to Sheldon ♻️ PS: Don’t be like Sheldon, don’t skip the manual! PPS: When was the last time you didn’t understand your old code?

The Medical Device Iceberg: What’s hidden beneath your product is what matters most. Your technical documentation isn’t "surface work". It’s the foundation that the Notified Body look at first. Let’s break it down ⬇ 1/ What is TD really about? Your Technical Documentation is your device’s identity card. It proves conformity with MDR 2017/745. It’s not a binder of loose files. It’s a structured, coherent, evolving system. Annexes II & III of the MDR guide your structure. Use them. But make it your own. 2/ The 7 essential pillars of TD: → Device description & specification → Information to be supplied by the manufacturer → Design & manufacturing information → GSPR (General Safety & Performance Requirements) → Benefit-risk analysis & risk management → Product verification & validation (including clinical evaluation) → Post-market surveillance Each one matters. Each one connects to the rest. Your TD is not linear. It’s a living ecosystem. Change one thing → It impacts everything. That’s why consistency and traceability are key. 3/ Tips for compiling TD: → Use one “intended purpose” across all documents → Apply the 3Cs: ↳ Clarity (write for reviewers) ↳ Consistency (same terms, same logic) ↳ Connectivity (cross-reference clearly) → Manage it like a project: ↳ Involve all teams ↳ Follow MDR structure ↳ Trace everything → Use “one-sheet conclusions” ↳ Especially in risk, clinical, V&V docs ↳ Simple, precise summaries → Avoid infinite feedback loops: ↳ One doc, one checklist, one deadline ↳ Define “final” clearly 4/ Best practices to apply: → Add a summary doc for reviewers → Update documentation regularly → Create a V&V matrix → Maintain URS → FRS traceability → Hyperlink related docs → Provide objective evidence → Use searchable digital formats → Map design & mfg with flowcharts Clear TD = faster reviews = safer time to market. Save this for your next compilation session. You don't want to start from scratch? Use our templates to get started: → GSPR, which gives you a predefined list of standards, documents and methods. ( https://lnkd.in/eE2i43v7 ) → Technical Documentation, which gives you a solid structure and concrete examples for your writing. ( https://lnkd.in/eNcS4aMG )

The Art of Structured Code: Next.js Application Architecture Excited to share this comprehensive file architecture blueprint for building robust web applications with Next.js, TypeScript, and Tailwind CSS! This structure implements industry best practices for: ✅ Clean separation of concerns ✅ Feature-based organization ✅ Type-safe development ✅ Scalable component design ✅ Modern routing patterns For example, in an eCommerce context, this architecture elegantly handles: • Product catalog organization • User authentication flows • Shopping cart state management • Checkout & payment processing • Order history tracking Whether you're building a digital storefront, SaaS platform, or content-rich application, starting with a well-organized structure saves countless hours and prevents technical debt. What file structure patterns have worked best in your projects? Let's connect and share insights! #nextjs #typescript #tailwindCSS #webdevelopment #softwarearchitecture #codequality #reactJS #frontenddevelopment #webapp #developerproductivity #cleancode #techbestpractices

Most import delays don't start at the port. They start at your desk - with bad paperwork. Standard Import Package: 1. Commercial Invoice  *Prepared By:* Exporter   *Primary User(s):* Customs, Broker, Importer  This document shows the sale between the buyer and seller. It lists the goods, their value, and payment terms. 2. Packing List *Prepared By:* Exporter   *Primary User(s):* Customs, Forwarder, 3PL      This list details how items are packed. It helps with inspections and logistics. 3. Bill of Lading / Air Waybill  *Prepared By:* Carrier or Forwarder   *Primary User(s):* Carrier, Customs      This is a contract for transport. It proves ownership and details the shipment. 4. Certificate of Origin *Prepared By:* Exporter / Chamber   *Primary User(s):* Customs      This document certifies where the goods come from. It can affect tariffs. 5. Import License / Permit *Prepared By:* Importer   *Primary User(s):* Customs      This license allows the goods to enter the country. It’s often required for certain products. 6. Insurance Certificate *Prepared By:* Insurer / Exporter   *Primary User(s):* Importer, Carrier  This certificate shows that goods are insured during transit. It protects against loss or damage. 7. Customs Declaration (e.g., Entry Summary, SAD) *Prepared By:* Broker/Importer   *Primary User(s):* Customs     This document provides details about the goods for customs clearance. 8. Other Documents *Prepared By:* Varies   *Primary User(s):* Customs, Importer  This may include inspection certificates, MSDS, or fumigation certificates. Common Mistakes & How to Prevent Them: 1. Missing or Incorrect HS Codes   *Prevention Strategy:* Use validated tariff classifications. 2. Inconsistent Descriptions  *Prevention Strategy:* Maintain a master data sheet for SKUs. 3. Wrong Incoterms *Prevention Strategy:* Align terms across all documents. 4. No Certificate of Origin *Prevention Strategy:* Pre-check FTA eligibility and requirements. 5. Incorrect Values *Prevention Strategy:* Ensure the declared value matches the invoice. 6. Wrong Consignee Details *Prevention Strategy:* Double-check against records. 7. Expired Import Permits *Prevention Strategy:* Track license validity in a compliance calendar. Final Compliance Checklist Before Submission: Are all documents complete & accurate?  Any region-specific requirements? Have all trade parties reviewed and confirmed? Smooth imports dont just happen. They're the result of documentation excellence. CTA: If you found this helpful, follow for more trade compliance insights.

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗵𝗮𝘀 𝗻𝗼 𝗮𝗴𝗲, 𝗻𝗼 𝗴𝗲𝗻𝗱𝗲𝗿, 𝗻𝗼 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗻𝗼 𝘀𝗸𝗶𝗻 𝗰𝗼𝗹𝗼𝗿. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘀 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲! 🦾♥️ #Technology isn’t just for the experts or those with STEM degrees. It impacts all of us - and to navigate this ongoing technological transformation, 𝘄𝗲 𝗔𝗟𝗟 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗶𝘁 & 𝗱𝗲𝗮𝗹 𝘄𝗶𝘁𝗵 𝗶𝘁! Because only through understanding, we build trust. As Deloitte Voice for Innovation & Tech Translator, I strive to make technology accessible, bridging the gap between technology and society, and between technology and business decision-makers. Some ideas to make tech more accessible: ✔️ 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲! Every company has a #responsibility to prepare employees for the #future. At Deloitte, we’ve already trained nearly 11,000 colleagues with our 𝗚𝗲𝗻-𝗔𝗜 𝗗𝗿𝗶𝘃𝗲𝗿’𝘀 𝗟𝗶𝗰𝗲𝗻𝘀𝗲, empowering them to use #AI responsibly and confidently. 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 for organizations committed to staying ahead. ✔️ 𝗦𝘁𝗮𝗿𝘁 𝘆𝗼𝘂𝗻𝗴: 𝗧𝗲𝗮𝗰𝗵 𝗸𝗶𝗱𝘀 𝗵𝗼𝘄 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝘄𝗼𝗿𝗸𝘀! I’ve collaborated with the 𝗛𝗮𝗰𝗸𝗲𝗿 𝗦𝗰𝗵𝗼𝗼𝗹 to teach coding in schools, because tech #education needs to start early. When I programmed HTML with young girls, they not only excelled but stayed focused - unlike the boys, who often got distracted by gaming. Early exposure is key to inspiring the next generation of tech talent! ✔️ 𝗕𝗿𝗶𝗻𝗴 𝘄𝗼𝗺𝗲𝗻 𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵 𝘁𝗮𝗯𝗹𝗲! To drive diversity in tech, we need more initiatives that actively include #women. Recently, I invited 30 women from my network to a robotics factory in Munich, where we 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲𝗱 𝗮 𝗿𝗼𝗯𝗼𝘁 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿. Experiences like this empower women and give them the confidence to claim their space in tech. ✔️ 𝗠𝗮𝗸𝗲 𝘁𝗲𝗰𝗵 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 𝗮 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝘆! We need more Tech Translators—people who can simplify complex technologies and make them accessible. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝗲 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 - 𝘀𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆 𝗶𝘀 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴. Tech isn’t just for developers; it’s for business leaders, educators, and anyone who wants to shape the future! 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗰𝘂𝗽 𝗼𝗳 𝘁𝗲𝗮! 🫖 Do you feel you already have enough #access to technology? If not, what’s missing for you? 𝘐𝘮𝘢𝘨𝘦 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘸𝘰𝘯𝘥𝘦𝘳𝘧𝘶𝘭: Mirjam Hagen 🫶🏽