How to Integrate AI in Software Development

𝘛𝘩𝘪𝘴 𝘸𝘢𝘴 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘐’𝘷𝘦 𝘣𝘦𝘦𝘯 𝘱𝘶𝘵𝘵𝘪𝘯𝘨 𝘵𝘰𝘨𝘦𝘵𝘩𝘦𝘳 𝘵𝘩𝘪𝘴 𝘸𝘦𝘦𝘬. 𝐍𝐨𝐭 𝐚𝐥𝐥 𝐀𝐈 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐚𝐫𝐞 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐞𝐪𝐮𝐚𝐥. Here’s how I integrate Microsoft Azure services to create AI that works for just about any business not the other way around. Want to know the secret sauce? 👇 7 Lessons from Building Scalable AI Solutions Customers Love: 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐧 𝐝𝐚𝐭𝐚. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐳𝐞𝐫 for structured ingestion. ↳ Automate preprocessing with 𝐀𝐳𝐮𝐫𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐀𝐩𝐩𝐬. ↳ Store data securely in 𝐀𝐳𝐮𝐫𝐞 𝐁𝐥𝐨𝐛 𝐒𝐭𝐨𝐫𝐚𝐠𝐞. 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐯𝐚𝐥𝐮𝐞. ↳ Focus on actionable insights, not noise. ↳ Leverage 𝐀𝐳𝐮𝐫𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 for advanced prep. ↳ Collaborate with end users for relevance. 𝐓𝐫𝐚𝐢𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 𝐭𝐡𝐚𝐭 𝐚𝐥𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐠𝐨𝐚𝐥𝐬. ↳ Test multiple architectures, like custom LLMs. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐌𝐋 and Azure OpenAI to streamline experimentation. ↳ Optimize for speed and scalability. 𝐃𝐞𝐩𝐥𝐨𝐲 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐧𝐠 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬. ↳ Host on 𝐀𝐳𝐮𝐫𝐞 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 for reliability. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 for seamless integration. ↳ Monitor deployment with feedback loops. 𝐌𝐚𝐤𝐞 𝐝𝐚𝐭𝐚 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐛𝐥𝐞 𝐚𝐧𝐝 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞. ↳ Index with 𝐀𝐳𝐮𝐫𝐞 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 Search. ↳ Store outputs in 𝐂𝐨𝐬𝐦𝐨𝐬 𝐃𝐁 for scalability. ↳ Ensure query optimization for real-time use. 𝐁𝐫𝐢𝐝𝐠𝐞 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐥𝐨𝐠𝐢𝐜. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 to support decisions. ↳ Automate workflows for better efficiency. ↳ Integrate insights directly into operations. 𝐆𝐨𝐯𝐞𝐫𝐧 𝐰𝐢𝐭𝐡 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐚𝐠𝐢𝐥𝐢𝐭𝐲 𝐢𝐧 𝐦𝐢𝐧𝐝. ↳ Use 𝐆𝐢𝐭 𝐅𝐥𝐨𝐰 for version control. ↳ Secure pipelines with 𝐂𝐡𝐞𝐜𝐤𝐦𝐚𝐫𝐱. ↳ Automate infrastructure with 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦. Which step will move your business forward today? ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #ArtificialIntelligence #AzureCloud #InnovationInTech #AITransformation #MachineLearningPipeline

🚀 Autonomous AI Coding with Cursor, o1, and Claude Is Mind-Blowing Fully autonomous, AI-driven coding has arrived—at least for greenfield projects and small codebases. We’ve been experimenting with Cursor’s autonomous AI coding agent, and the results have truly blown me away. 🔧 Shifting How We Build Features In a traditional dev cycle, feature specs and designs often gloss over details, leaving engineers to fill in the gaps by asking questions and ensuring alignment. With AI coding agents, that doesn’t fly. I once treated these models like principal engineers who could infer everything. Big mistake. The key? Think of them as super-smart interns who need very detailed guidance. They lack the contextual awareness that would allow them to make all the micro decisions that align with your business or product direction. But describe what you want built in excruciating detail, it's amazing the quality of the results you can get. I recently built a complex agent with dynamic API tool calling—without writing a single line of code. 🔄 My Workflow ✅ Brain Dump to o1: Start with a raw, unstructured description of the feature. ✅ Consultation & Iteration: Discuss approaches, have o1 suggest approaches and alternatives, settle on a direction. Think of this like the design brainstorm collaboration with AI. ✅ Specification Creation: Ask o1 to produce a detailed spec based on the discussion, including step-by-step instructions and unit tests in Markdown. ✅ Iterative Refinement: Review the draft, provide more thoughts, and have o1 update until everything’s covered. ✅ Finalizing the Spec: Once satisfied, request the final markdown spec. ✅ Implementing with Cursor: Paste that final spec into a .md file in Cursor, then use Cursor Compose in agent mode (Claude 3.5 Sonnet-20241022) and ask it to implement the feature in the .md file. ✅ Review & Adjust: Check the code and ask for changes or clarifications. ✅ Testing & Fixing: Instruct the agent to run tests and fix issues. It’ll loop until all tests pass. ✅ Run & Validate: Run the app. If errors appear, feed them back to the agent, which iteratively fixes the code until everything works. 🔮 Where We’re Heading This works great on smaller projects. Larger systems will need more context and structure, but the rapid progress so far is incredibly promising. Prompt-driven development could fundamentally reshape how we build and maintain software. A big thank you to Charlie Hulcher from our team for experimenting with this approach and showing us how to automate major parts of the development lifecycle.

Exactly a year ago, we embarked on a transformative journey in application modernization, specifically harnessing generative AI to overhaul one of our client’s legacy systems. This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic business objectives Reflecting on this journey, here are the key lessons and outcomes we achieved through Gen AI in application modernization: [1] Assess Application Portfolio. We started by analyzing which applications were both outdated and critical, identifying those with the highest ROI for modernization.  This targeted approach helped prioritize efforts effectively. [2] Prioritize Practical Use Cases for Generative AI. For instance, automating code conversion from COBOL to Java reduced the overall manual coding time by 60%, significantly decreasing costs and increasing efficiency. [3] Pilot Gen AI Projects. We piloted a well-defined module, leading to a 30% reduction in time-to-market for new features, translating into faster responses to market demands and improved customer satisfaction. [4] Communicate Success and Scale Gradually. Post-pilot, we tracked key metrics such as code review time, deployment bugs, and overall time saved, demonstrating substantial business impacts to stakeholders and securing buy-in for wider implementation. [5] Embrace Change Management. We treated AI integration as a critical change in the operational model, aligning processes and stakeholder expectations with new technological capabilities. [6] Utilize Automation to Drive Innovation. Leveraging AI for routine coding tasks not only freed up developer time for strategic projects but also improved code quality by over 40%, reducing bugs and vulnerabilities significantly. [7] Opt for Managed Services When Appropriate. Managed services for routine maintenance allowed us to reallocate resources towards innovative projects, further driving our strategic objectives. Bonus Point: Establish a Center of Excellence (CoE). We have established CoE within our organization. It spearheaded AI implementations and established governance models, setting a benchmark for best practices that accelerated our learning curve and minimized pitfalls. You could modernize your legacy app by following similar steps! #modernization #appmodernization #legacysystem #genai #simform — PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies.

The more I used AI to build, the less magical it felt. After spending weeks trying to rebuild our platform with Claude/ChatGPT, I realized a hard truth: What started as a productivity hack turned into a full-time prompt engineering job. Don't get me wrong, AI is mind-blowing at what it does. But you have to think of it the right way: Even with recent updates like Claude Code + Sonnet 3.7, AI still needs your architectural vision and continuous feedback to deliver production-quality results. I recently shared my experience with AI coding, and dozens of engineers and leaders jumped in with their own battle-tested insights (original post in comments). These were the strongest insights and recommendations: - Design the architecture first, then let AI do the implementation following YOUR architecture. - Instead of asking AI to build the whole car at once, ask it to build individual parts separately. - Iterate and guide: Aiming for perfection on the first prompt response will not work. - Start with AI-written unit tests to ensure your expectations align with the output (hello TDD 😉). - And finally, remember that AI can only code as well as you can architect and explain. These insights are from engineering professionals who are making AI work instead of creating more work. The gap between using AI and using it WELL is wider than most realize, and the teams closing this gap are the ones getting real results. What's your most effective technique for integrating AI into your development process? — Big thanks to Antons Kumkovs, Michael Rollins, Winner Emetuche, Drew Adams, Domingo Gómez García, Ryan Booth, Michael Fanous, David Cornelson, Youssef El Hassani, and Michael L. whose comments are included in the carousel 👇

I've been creating a lot of AI agents lately and here are my top 3 tips for developers looking to incorporate AI into their applications: 1. Understand how your AI agent works under the hood There are a variety of AI solutions available, from ML models that specialize in a single purpose like object detection, to multiple styles of generative models that can respond to prompts, to Retrieval Augmented Generation (RAG) AI pipelines that use provided context to enhance the model's knowledge base. Understanding what type of solution you're using is important to knowing its capabilities and limitations. You can check out my blog post on RAG AI to learn more about how Squid Cloud's AI features are implemented: https://lnkd.in/dcqANVrA 2. Decide how deep you want to go Are you looking for a low-code/no-code solution? Are you hoping to implement some AI features through code without becoming an AI developer? Do you want to get hands-on with context chunking, vector embedding, storage, and retrieval? Or do you want to go all-in and try your hand at fine-tuning a custom model? If you're looking to incorporate AI into an app, you probably want a low-code option or a code-based option that doesn't require model or pipeline customization. AI development is very rewarding, but it's a job in itself, so recognize that if you spend time perfecting your model or pipeline, you won't be spending time on your app. 3. Start small Like traditional software development, you want to work on small components, verify they are working as expected through testing, and then build on them. This allows you to hone your prompts and better understand how your chosen model responds. When you have an AI agent that can take multiple actions based on prompts, it can be challenging to root out the cause of errors in the LLM's decision-making. Adding one small action at a time and testing it thoroughly reduces time spent bug bashing, just like it would when writing any other code. Want to learn how to incorporate AI into your web apps? Join me on my weekly live streams or join one of our bi-weekly hands-on webinars. You can reach out to me here on LinkedIn or on our Discord server for the details 👇 https://lnkd.in/d-FmwKu5