Tips for Balancing Speed and Quality in AI Coding

Most developers treat AI coding agents like magical refactoring engines, but few have a system, and that's wrong. Without structure, coding with tools like Cursor, Windsurf, and Claude Code often leads to files rearranged beyond recognition, subtle bugs, and endless debugging. In my new post, I share the frameworks and tactics I developed to move from chaotic vibe coding sessions to consistently building better, faster, and more securely with AI. Three key shifts I cover: -> Planning like a PM – starting every project with a PRD and modular project-docs folder radically improves AI output quality -> Choosing the right models – using reasoning-heavy models like Claude 3.7 Sonnet or o3 for planning, and faster models like Gemini 2.5 Pro for focused implementation -> Breaking work into atomic components – isolating tasks improves quality, speeds up debugging, and minimizes context drift Plus, I share under-the-radar tactics like: (1) Using .cursor/rules to programmatically guide your agent’s behavior (2) Quickly spinning up an MCP server for any Mintlify-powered API (3) Building a security-first mindset into your AI-assisted workflows This is the first post in my new AI Coding Series. Future posts will dive deeper into building secure apps with AI IDEs like Cursor and Windsurf, advanced rules engineering, and real-world examples from my projects. Post + NotebookLM-powered podcast https://lnkd.in/gTydCV9b

I've been using AI coding tools for a while now & it feels like every 3 months the paradigm shifts. Anyone remember putting "You are an elite software engineer..." at the beginning of your prompts or manually providing context? The latest paradigm is Agent Driven Development & here are some tips that have helped me get good at taming LLMs to generate high quality code. 1. Clear & focused prompting ❌ "Add some animations to make the UI super sleek" ✅ "Add smooth fade-in & fade out animations to the modal dialog using the motion library" Regardless of what you ask, the LLM will try to be helpful. The less it has to infer, the better your result will be. 2. Keep it simple stupid ❌ Add a new page to manage user settings, also replace the footer menu from the bottom of the page to the sidebar, right now endless scrolling is making it unreachable & also ensure the mobile view works, right now there is weird overlap ✅ Add a new page to manage user settings, ensure only editable settings can be changed. Trying to have the LLM do too many things at once is a recipe for bad code generation. One-shotting multiple tasks has a higher chance of introducing bad code. 3. Don't argue ❌ No, that's not what I wanted, I need it to use the std library, not this random package, this is the 4th time you've failed me! ✅ Instead of using package xyz, can you recreate the functionality using the standard library When the LLM fails to provide high quality code, the problem is most likely the prompt. If the initial prompt is not good, follow on prompts will just make a bigger mess. I will usually allow one follow up to try to get back on track & if it's still off base, I will undo all the changes & start over. It may seem counterintuitive, but it will save you a ton of time overall. 4. Embrace agentic coding AI coding assistants have a ton of access to different tools, can do a ton of reasoning on their own, & don't require nearly as much hand holding. You may feel like a babysitter instead of a programmer. Your role as a dev becomes much more fun when you can focus on the bigger picture and let the AI take the reigns writing the code. 5. Verify With this new ADD paradigm, a single prompt may result in many files being edited. Verify that the code generated is what you actually want. Many AI tools will now auto run tests to ensure that the code they generated is good. 6. Send options, thx I had a boss that would always ask for multiple options & often email saying "send options, thx". With agentic coding, it's easy to ask for multiple implementations of the same feature. Whether it's UI or data models asking for a 2nd or 10th opinion can spark new ideas on how to tackle the task at hand & a opportunity to learn. 7. Have fun I love coding, been doing it since I was 10. I've done OOP & functional programming, SQL & NoSQL, PHP, Go, Rust & I've never had more fun or been more creative than coding with AI. Coding is evolving, have fun & let's ship some crazy stuff!

𝗧𝗟;𝗗𝗥: AWS Distinguished Engineer Joe Magerramov's team achieved 10x coding throughput using AI agents—but success required completely rethinking their testing, deployment, and coordination practices. Bolting AI onto existing workflows will create crashes, not breakthroughs. Joe M. is an AWS Distinguished Engineer who has architected some of Amazon's most critical infrastructure, including foundational work on VPCs and AWS Lambda. His latest insights on agentic coding (https://lnkd.in/euTmhggp) come from real production experience building within Amazon Bedrock. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗣𝗮𝗿𝗮𝗱𝗼𝘅 Joe's team now ships code at 10x typical high-velocity teams—measured, not estimated. About 80% of committed code is AI-generated, but every line is human-reviewed. This isn't "vibe coding." It's disciplined collaboration between engineers and AI agents. But here's the catch: At 10x velocity, the math changes completely. A bug that occurs once a year at normal speed becomes a weekly occurrence. Their team experienced this firsthand. 𝗧𝗵𝗲 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗚𝗮𝗽 Success required three fundamental shifts:  • 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 - They built high-fidelity fakes of all external dependencies, enabling full-system testing at build time. Previously too expensive; now practical with AI assistance.  • 𝗖𝗜𝗖𝗗 𝗿𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗲𝗱 - Traditional pipelines taking hours to build and days to deploy create "Yellow Flag" scenarios where dozens of commits pile up waiting. At scale, feedback loops must compress from days to minutes.  • 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗻𝘀𝗶𝘁𝘆 - At 10x throughput, you're making 10x more architectural decisions. Asynchronous coordination becomes the bottleneck. Their solution: co-location for real-time alignment. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗧𝗢𝘀 Don't just give your teams AI coding tools. Ask:  • Can your CI/CD handle 10x commit volume?  • Will your testing catch 10x more bugs before production?  • Can your team coordinate 10x faster? The winners won't be those who adopt AI first—they'll be those who rebuild their development infrastructure to sustain AI-driven velocity.

Code much faster with AI, but at what cost… Ignore quality and maintainability issues? Or spend hours reviewing code we haven’t written? A Theodo team explored ingenious ways to break that trade-off. Antoine de Chassey , Hugo Borsoni, Thibault Lemery and Margaux Theillier led a 6-step kaizen on accelerating AI-code reviews without sacrificing quality. Based on extensive experience, they’ve identified that AI is much more reliable when it is building components by copying an existing good example. So they tagged good examples they called blueprints. And then asked the AI to make it explicit, on the code generated, whether it was able to copy a blueprint or not. This allowed them to focus their code reviews on all the places where the AI wasn’t able to copy a blueprint, places that are much more prone to quality issues. A very ingenious way to review all the code, ensuring maximum quality, while focusing attention on the less reliable places. Well done for that great example of Lean Tech in action at Theodo!

I've spent 9 months figuring out what actually works with AI coding tools—especially on messy, real-world codebases. The breakthrough? Stop letting AI write code until you've reviewed a written plan. Here's the flow that I keep seeing when researching, and Boris has done a great job in collecting the whole thing in his blog: 1. Research Phase: Don't accept verbal summaries. Force deep reads into persistent files. "Read auth/middleware deeply. Write findings in research.md with intricacies." Written artifacts = review surfaces. Catches misunderstandings before they become broken implementations. 2. Planning Phase: Request detailed plans in plan.md—with code snippets, file paths, trade-offs. Not the built-in plan mode (very important!). Custom markdown files you control. 3. Annotation Cycle (the critical part): Review the plan in your editor. Add inline notes directly: - "This breaks OAuth flow" - "Use existing UserService instead" - "Security: validate input here" Send annotated plan back. Repeat 1-6 times until it's right. This is where the main thinking happens! 4. Then—and only then—implement This prevents the most expensive failure: code that works in isolation but breaks everything around it. Pro tip: For standard features, provide reference implementations from open source. Claude with a concrete example >>> Claude designing from scratch. The workflow feels slower at first. But catching architectural mistakes in a 50-line plan.md beats debugging a 500-line implementation that went wrong from line 1. This process is now called RPI (Reserch, Plan, Implement) - have you tried this in your workflows yet? https://lnkd.in/dMP7dCgc

AI makes developers faster. But what happens when that value comes at the cost of actually understanding what you're building? When researchers at Anthropic tested 52 professional developers learning an unfamiliar Python library, the AI-assisted group scored 17% lower on conceptual understanding, code reading, and debugging — across all experience levels. There was also no significant difference in task completion time. 🔴 The biggest skill gap was in debugging. The control group hit a median of 3 errors during the task versus just 1 for the AI group. Working through those errors is what made the concepts stick. 🔴 Not all AI usage was equal. Developers who asked conceptual questions scored 65-86% on the skills quiz. Those who just delegated code generation? 24-39%. 🔴 The AI users felt it, too. Several described themselves as feeling "lazy" and wished they'd engaged more deeply with the material. To be clear, the finding isn't "don't use AI." It's that delegation and learning are fundamentally different activities — and most developers are defaulting to delegation. If you want to get the best of speed AND learning, consider these ideas: 1️⃣ Separate performance tasks from learning tasks. When your team already knows the domain, let AI accelerate delivery. When they're onboarding to something new, encourage AI for explanations and conceptual questions. 2️⃣ Stop optimizing away all friction. Debugging isn't all wasted time — it's where understanding forms. That investment comes in handy when you're trying to debug a P0 in production or explain logic to business leaders. 3️⃣ Coach high-signal interaction patterns. "Explain how this concurrency model works" produces very different outcomes than "write the function for me." We obsess over how fast AI helps developers ship, but we should think slightly longer term about the impact of that speed, and what it means for long-term learning and retention. Full research breakdown in this week's RDEL (link in comments). How is your team balancing AI speed with skill development?

𝐓𝐡𝐞 𝐰𝐢𝐧𝐧𝐢𝐧𝐠 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐞𝐚𝐫𝐥𝐲-𝐬𝐭𝐚𝐠𝐞 𝐀𝐈 𝐬𝐭𝐚𝐫𝐭𝐮𝐩𝐬 — 𝐏𝐚𝐫𝐭 𝟓/𝟓 𝐄𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐄𝐯𝐚𝐥𝐬. This is the final part of the series — and the most important. In Parts 1–4, the discussion was on design partners, engineers watching users, AI-generated code, and daily shipping. All of that gives you speed. This part is about making sure that speed does not destroy your product quality. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐰𝐢𝐭𝐡 𝐬𝐩𝐞𝐞𝐝. This is where most early-stage AI teams fall short. They can build fast. They can ship fast. But they break things as fast as they fix them. A prompt change that improves one use case quietly degrades three others. A model upgrade slowly degrades quality in ways nobody catches until a customer complains. With AI product outputs being probabilistic, "correct" is often a judgement call. And the same input can produce different outputs on different days if you change your prompts, your model version, or your retrieval pipeline. 𝐍𝐨𝐧-𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦. The winning teams build measurement and observability into every layer — dev, CI/CD, staging, and production. The eval framework is the measurement system. Before you even generate your first-line of code, you have to get the test and eval framework setup. It could be deterministic checks, deterministic UI automations, LLMs as judge for generated AI quality checks, and a way to score the results across agents and systems. 𝐓𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐫𝐚𝐭𝐜𝐡𝐞𝐭. The hardest thing I have seen across portfolio companies, is building an end-to-end system for a way to move code in an autonomous way from dev to stage to production, without worrying that things may have broken. The most important thing that compounds is bringing test cases from production to stage to development in reverse. The more robust your eval infrastructure, the faster you can move. 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭 — 𝐚𝐥𝐥 𝐟𝐢𝐯𝐞 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. When all five are in place — real design partners, engineers next to users, AI-generated code with senior control, daily shipping, and eval-instrumented testing — the product quality compounds at a rate that is almost impossible for competitors to match. You are building a machine that converts user insight into shipped product in days, with quality that improves with every iteration. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: If you are an early-stage AI founder and you do not have all five in place, please fix it now. Start with design partners — everything else flows from there. The product quality that wins markets is not built in a lab. It is built in the field, with real users, at speed, with a safety net that gets stronger every day. Love to hear your experience in parts or full, if you are practicing any of the above.

The $285B Claude Code software selloff wasn't about technology. It was about expectations: CTOs are under massive pressure for 2-3x speed improvements. But here's what most are getting wrong: They're optimizing old processes instead of shifting to new development models. After working with enterprise teams for 20 years, I've seen this pattern before. The companies winning aren't just coding faster. They're coding differently. Here's the framework that's actually delivering results: 1/ Start with zero-code validation through vibe coding: ↳ Business teams describe what they want, AI builds it. ↳ No developer bottlenecks for testing concepts. ↳ Perfect for stakeholder demos before major investment. ↳ ROI: Validate market fit without burning engineering resources. The smartest CTOs I know use this to kill bad ideas fast. And fund good ones with confidence. Tools: Lovable, Base44, Replit and Stagewise. 2/ Supercharge your current developers through AI assisted coding: ↳ 25-30% productivity boost without changing your team. ↳ AI handles repetitive work, developers focus on architecture, scale and testing. ↳ Context engineering behind the scenes orchestrates multiple AI calls. ↳ Your developers stay in control of critical decisions. Here's what most people miss: Spotify's most productive developers haven't written boilerplate code since December. Let that sink in. They're designing systems while AI handles implementation. The shift is accelerating: ↳ 2025: 80% AI-Assisted, 20% Autonomous ↳ 2026: 20% AI-Assisted, 80% Autonomous Tools: Cursor, Google Antigravity, and Github Copilot. 3/ Deploy autonomous development through agentic coding: ↳ Define business outcomes, while AI plans and executes. ↳ multi-step development with reasoning loops. ↳ ROI: Accelerate time-to-market for new products by 60-80%. This is where the real speed improvements come from. Not faster typing. Autonomous execution. Tools: Claude Code, OpenAI Codex, and Google CLI Agentic Coding. Most companies are still stuck optimizing their current development process. The leaders who will dominate 2026? They're moving up the AI maturity levels for complex development work. Speed isn't just an advantage anymore. It's about survival. What level is your development team operating at? Share below. ♻️ Share with someone who needs to see this. ➕ Follow me, Ashley Nicholson, for more tech insights.

Most AI coding tools don’t save time. They quietly create more work. Here’s what I learned. After testing across 20+ real client projects, the question changed: not “which tool is smartest?” but “which one breaks the least in production?” Here’s how the tools actually stack up: • GitHub Copilot → best for boilerplate, tests, docs • Cursor → best for large codebases + multi-file edits • Replit AI → best for fast MVPs and prototypes • Tabnine → best for regulated / secure environments • Crew AI → best for agent-driven workflows and internal prototype building And here’s the part no one tells you: AI is cheap until it’s not. Hidden costs stack fast: • token usage • debugging bad outputs • extra code reviews • production mistakes The problem now is not writing code. It is reviewing AI-generated code without losing control. Smart teams are not using more tools. They are using stricter systems: • pilot first, don’t blindly adopt • measure errors + cost per output • add guardrails (linters, CI, human review) AI should assist. Not take the wheel. If you had to pick one: Would you optimise for speed (faster code) or control (fewer bugs and fixes)? Share with your network ➕ Follow for more AI educational content ___________________________________________ 👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

Velocity wins headlines. Reliability wins customers. When one tool can crank out a billion accepted lines of code a day, the bottleneck shifts from creation to confidence. Fast is no longer enough. The question is whether you can trust what ships. My playbook for keeping quality ahead of velocity: 1. Automate the obvious. Let AI handle scaffolding, linting, boilerplate. 2. Ruthlessly delete. Remove any redundant code. Simplify. 3. Freeze best practice into reusable modules. Publish a churn formula once, reuse it everywhere, and metric drift dies before it starts. 4. Codify your contribution standards. Help AI ship code you’ll actually accept by writing the kind of guidelines you’d expect from a great hire. 5. Make failures loud and early. Good observability is cheaper than perfect code. Scale isn’t scary if trust scales with it. Nail that balance, and a billion lines a day becomes an advantage, not a liability.