Software Development Lifecycle In Engineering

What if your LLM could reuse work and respond 5-10× faster? That’s exactly what LMCache delivers. What is LMCache? It’s the open-source “KV cache layer” for LLMs — designed to store and reuse key/value caches across queries, sessions and even engines. Built for high-volume, long-context systems. Evaluations show up to 15× throughput improvements when paired with engines like vLLM. Why This Matters Right Now Latency kills UX. Every extra millisecond waits hit adoption. LMCache slashes response time by re-using caches. GPU cycles cost money. Re-computation means wasted resources. LMCache allows reuse across workloads, reducing GPU load. Context & multi-round workflows are exploding. RAG systems, agent pipelines, conversational contexts — LMCache fits them all. It’s production-ready and open-source. No black-box: you can inspect, integrate, extend. Typical Use Cases: -Agentic systems that make multi-turn decisions -RAG pipelines that reuse retrievalable contexts -Long-form applications (document processing + summarization) -Multi-engine inference clusters / cloud-scale deployments Plug into your engine and enable KV-cache reuse across queries & threads. If you’re building LLM-based systems for scale, this isn’t one more library — it’s a fundamental architecture upgrade. Mark this: The future of LLM inference isn’t just bigger models — it’s smarter reuse.

Building software without a strategy is like buying a Ferrari to sit in traffic. Looks good, goes nowhere. Too many companies rush into development because they feel the pressure to "move fast." They hire, they spend, they build But without a clear direction, all they’re really doing is accelerating waste and burn rate. The truth is, clarity beats speed every time. When you know exactly what problem you’re solving, why it matters, and how it connects to the bigger picture, every line of code becomes an investment rather than a gamble. The Ferrari is powerful, but if the road ahead is blocked, you’re still stuck. Strategy clears the road. Don’t just build. Plan, refine, and then scale. That’s how you actually get somewhere.

In the last few months, I have explored LLM-based code generation, comparing Zero-Shot to multiple types of Agentic approaches. The approach you choose can make all the difference in the quality of the generated code. Zero-Shot vs. Agentic Approaches: What's the Difference? ⭐ Zero-Shot Code Generation is straightforward: you provide a prompt, and the LLM generates code in a single pass. This can be useful for simple tasks but often results in basic code that may miss nuances, optimizations, or specific requirements. ⭐ Agentic Approach takes it further by leveraging LLMs in an iterative loop. Here, different agents are tasked with improving the code based on specific guidelines—like performance optimization, consistency, and error handling—ensuring a higher-quality, more robust output. Let’s look at a quick Zero-Shot example, a basic file management function. Below is a simple function that appends text to a file: def append_to_file(file_path, text_to_append): try: with open(file_path, 'a') as file: file.write(text_to_append + '\n') print("Text successfully appended to the file.") except Exception as e: print(f"An error occurred: {e}") This is an OK start, but it’s basic—it lacks validation, proper error handling, thread safety, and consistency across different use cases. Using an agentic approach, we have a Developer Lead Agent that coordinates a team of agents: The Developer Agent generates code, passes it to a Code Review Agent that checks for potential issues or missing best practices, and coordinates improvements with a Performance Agent to optimize it for speed. At the same time, a Security Agent ensures it’s safe from vulnerabilities. Finally, a Team Standards Agent can refine it to adhere to team standards. This process can be iterated any number of times until the Code Review Agent has no further suggestions. The resulting code will evolve to handle multiple threads, manage file locks across processes, batch writes to reduce I/O, and align with coding standards. Through this agentic process, we move from basic functionality to a more sophisticated, production-ready solution. An agentic approach reflects how we can harness the power of LLMs iteratively, bringing human-like collaboration and review processes to code generation. It’s not just about writing code; it's about continuously improving it to meet evolving requirements, ensuring consistency, quality, and performance. How are you using LLMs in your development workflows? Let's discuss!

Back in the day I worked on a major platform revamp. The objective was to remain competitive and meet regulations. At the same time our biggest competitor was also upgrading their system. Both were huge, multi-year projects with lots of investment. Our competitor started ahead of us. But, we had a key strategy: → Rapid adoption with shorter cycles! Instead of waiting for a big reveal after three years, we rolled out capability periodically. This let us constantly improve our platform based on real-time customer feedback. Our competitor went with a traditional approach, aiming for one major release at the end. The result? By the end of three years, we had not only improved our NPS score but also taken a larger part of the market share! Our strategy kept us agile and responsive, letting us adapt quickly to market changes and customer needs. Our competitor launched an outdated system that couldn't meet current demands. Here's what we learned: 1. Customer-Centric Development: ↳ Frequent releases allowed us to gather and implement customer feedback continuously, enhancing user satisfaction and engagement. 2. Iterative Improvement: ↳ Rapid iteration enabled us to pivot quickly and address any issues or new opportunities that arose during the development process. 3. Competitive Edge: ↳ By staying ahead of trends and being first to market with new features, we were able to capture more market share and strengthen our position. In tech, speed isn't just about being fast—it's about efficient adoption. 👉 Rapid adoption and continuous iteration transforms a good product into a great one, and adds a massive competitive advantage to the company. It can also ensure survival.

🛠️ What Running Test Automation Involves 🔎 📌 On-Demand Test Automation: This approach allows teams to execute test automation whenever there is a requirement to do so. It can be integrated into various stages of the development process, such as during product development, the addition of new features, or when there are new developments in testing methodologies. 📌 Timed Test Automation: Test automation can be triggered based on time. Initially, automation may take minutes due to fewer iterations, but as the number of iterations and version numbers increases, it may take hours. Running automation tests overnight is a common practice to analyze new changes to the software. 📌 Activity-Based Test Automation: As the application grows, developers shift from time-based triggers to activity-based triggers. The goal here is to target changes in the application, which can include updates, new features, or modifications to the existing features. 📌 Regression Testing: Test automation is particularly useful for regression testing, where previously implemented functionalities are tested to ensure that new changes or updates haven't introduced any unintended side effects or regressions. 📌 Parallel Execution: To speed up the testing process, automation tools often support parallel execution of test cases across multiple environments or devices. Parallel execution helps reduce the overall testing time, allowing teams to achieve faster feedback cycles and accelerate time-to-market for their products. 📌 Integration with Continuous Integration/Continuous Deployment (CI/CD): Test automation can be seamlessly integrated into CI/CD pipelines to automate the testing process as part of the overall software delivery pipeline. Automated tests can be triggered automatically whenever new code changes are committed, ensuring that each code change is thoroughly tested before deployment to production. 📌 Reporting and Analysis: Test automation tools often provide detailed reports and analytics on test execution results, including test coverage, pass/fail status, execution time, and more. These reports help stakeholders make informed decisions about the quality of the software and prioritize areas for improvement. 📌 Maintenance and Refactoring: Test automation requires ongoing maintenance and refactoring to keep test suites up to date with changes in the application codebase. As the application evolves, test scripts may need to be updated or refactored to accommodate new features or changes in functionality. 📌 Scalability and Flexibility: Test automation frameworks should be scalable and flexible to accommodate the evolving needs of the organization and the application. Scalable automation frameworks can handle large test suites efficiently, while flexible frameworks allow for easy customization and extension to support new testing requirements.

Best Practices for Writing Modular and Reusable Code ➊ Design ➟ Single Responsibility Each module/class/function should do one thing. This is the core of the Single Responsibility Principle (SRP), a pillar of SOLID design. ➟ Loose Coupling Modules should have minimal dependencies. This ensures easier reuse and lower maintenance cost. ➟ High Cohesion Related functionality should be grouped together for better clarity and maintainability. ➋ Structure ➟ Small Functions Functions should do one thing and do it well. This improves readability and testability. ➟ Clear Interfaces Well-defined APIs/interfaces enable easy integration and replacement. ➟ Consistent Naming Using consistent, descriptive names helps other developers understand the code quickly. ➌ Reuse ➟ Libraries Common functionality should be abstracted into libraries/modules. ➟ Generic Code Use parameterization/generics/templates where appropriate to maximize reuse. ➟ Configuration Avoid hardcoding values, use configs for flexibility. ➍ Testing ➟ Unit Tests Essential for verifying small, isolated pieces of code. ➟ Mocking Facilitates testing in isolation, independent of dependencies. ➟ Coverage Strive for high test coverage to ensure reliability. ➎ Documentation ➟ Comments Explain why (not just what) in the code. ➟ Readme Every module/library should have clear usage instructions. ➟ Examples Usage examples are extremely helpful for onboarding and adoption. ➏ Maintainability ➟ Refactor Regularly Tackle technical debt before it grows. ➟ Code Reviews Peer reviews catch issues and spread knowledge. ➟ Follow Standards Consistency (via code style guides and conventions) prevents confusion.

Struggling to keep your software projects on track? 😕 Misunderstandings in the early stages of development can send even the best teams into costly cycles of rework. Imagine a process where everything is clear from the get-go. Right at the outset, QA teams, including automation engineers and manual testers, can craft test cases in plain English. This approach isn't just about efficiency, it's about setting your project up for success from day one. By establishing all crucial functionality early, this method dramatically streamlines the entire development cycle. It eliminates the chaos and keeps everyone aligned on the project goals. With clear and detailed specifications from the start, engineers receive a roadmap that significantly reduces the need to backtrack and rewrite. This not only saves time but also conserves valuable resources. The payoff? You could see up to a 𝟑𝟓% 𝐬𝐚𝐯𝐢𝐧𝐠𝐬 𝐢𝐧 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐢𝐦𝐞, thanks to a drastic reduction in misinterpretations and overlooked details. Let’s rethink the effectiveness of our initial development stages. What could your team achieve with all that recovered time? #SDLC #QualityAssurance #SoftwareDevelopment #AITestingTools

What is the LLM Mesh AI architecture and why your enterprise may need it? Key highlights include: • Introducing the LLM Mesh, a new architecture for building modular, scalable agentic applications • Standardizing interactions across diverse AI services like LLMs, retrieval, embeddings, tools, and agents • Abstracting complex dependencies to streamline switching between OpenAI, Gemini, HuggingFace, or self-hosted models • Managing over seven AI-native object types including prompts, agents, tools, retrieval services, and LLMs • Supporting both code-first and visual low-code agent development while preserving enterprise control • Embedding safety with human-in-the-loop oversight, reranking, and model introspection • Enabling performance and cost optimization with model selection, quantization, MoE architectures, and vector search Insightful: Who should take note • AI architects designing multi-agent workflows with LLMs • Product teams building RAG pipelines and internal copilots • MLOps and infrastructure leads managing model diversity and orchestration • CISOs and platform teams standardizing AI usage across departments Strategic: Noteworthy aspects • Elevates LLM usage from monolithic prototypes to composable, governed enterprise agents • Separates logic, inference, and orchestration layers for plug-and-play tooling across functions • Encourages role-based object design where LLMs, prompts, and tools are reusable, interchangeable, and secure by design • Works seamlessly across both open-weight and commercial models, making it adaptable to regulatory and infrastructure constraints Actionable: What to do next Start building your enterprise LLM Mesh to scale agentic applications without hitting your complexity threshold. Define your abstraction layer early and treat LLMs, tools, and prompts as reusable, modular objects. Invest in standardizing the interfaces between them. This unlocks faster iteration, smarter experimentation, and long-term architectural resilience. Consideration: Why this matters As with microservices in the cloud era, the LLM Mesh introduces a new operating model for AI: one that embraces modularity, safety, and scale. Security, governance, and performance aren’t bolted on and they’re embedded from the ground up. The organizations that get this right won’t just deploy AI faster they’ll actually deploy it responsibly, and at scale.

I’ve led significant products and successfully brought them to the finish line. Every time I take on a massive product, the first thing I do is plan for the pre-launch and post-launch activities. I brainstorm with product, UX, engineering, and QA teams to list all the different areas, milestones, risks, and dependencies. When building a new product or introducing significant enhancements, there are hundreds of things to track and align to ensure a timely launch and avoid getting stuck in an endless development cycle. Remember, coding-related activities contribute only 30% to a product's success. The remaining 70% comes from ideation, planning, communication, and adoption. Here are some of the crucial activities I focus on when I start working on a product launch: 1. Visualize the Go-Live Understand this: at this stage, most teams don’t even have a running product or final mockups yet. But we do have a high-level understanding of what it will look like. So, we start by imagining the change already in the hands of our users. Imagine the go-live day and map out every activity that comes to mind. This includes: - Product is live for consumers - Stakeholders are communicated with - A go-live support center is established - Customers and support teams are well-educated and informed Think about users, - What are they feeling? - What are they missing? - What questions do they have? - What challenges are they facing? - What do we wish we had done differently? We ensure that our entire team—engineering, product, support, marketing—knows how to support and communicate with both internal and external users about the new changes. 2. Visualize the Pre-Go-Live Map out every activity leading up to go-live. What needs to happen right before go-live? This includes: - Coding is completed - Extensive testing is conducted - Preparation for deployment - Production infrastructure is configured - Education, training, and pre-go-live communication 3. Create a Master List These brainstorming sessions result in a master list of activities—from initial development to launch. This list ensures we cover every critical step, such as: - UX design - Development - In-sprint testing - DevOps activities - Regression testing Additionally, it includes activities related to: - Risk management - Dependencies - Communication - Education & training Remember to balance the big picture with the details. We use JIRA to plan and track our day-to-day work. It’s invaluable for tracking details, but it can also be overwhelming. That’s why we need a high-level view with milestones, dependencies, and potential risks. This top-down planning approach—working backward from the launch to where we are today—has transformed how I manage and deliver big initiatives, and it can do the same for you. What approach do you follow when planning a significant initiative? Let’s discuss.

If I could give one piece of advice to every new developer out there, it’d be this: Stop diving into code before you know where you’re swimming. Yes, learning syntax matters. Yes, clean code matters. But the real secret to writing reliable software isn’t in your editor, it’s in your planning. I’ve watched too many developers (my past self included) jump straight into building something “cool” and end up tangled in their own spaghetti. Why? Because they never stopped to plan how it should work in the first place. These days, I plan everything. I’m building a containerization tool in Rust, and before I even touch a line of code, I’m mapping the entire flow: Menu → js/rust/py → create temp dir → copy source → etc. Then I drill down. Each step gets its own sub-map, naming conventions, ignored files, internal logic, all of it. It’s like creating a “map of maps” so there’s zero guesswork later. Planning might sound boring, but you know what’s worse? Rewriting the same half-baked idea three times because you didn’t plan it right the first time. I’d say planning is easily 90% - 95% of the job I personally use LucidChart for this and it’s pretty good, another good option is draw.io if you like free and simple :D #SystemDesign #ProjectPlanning #SoftwareDesign #ArchitectureMatters #TechStrategy #RustLang #RustProgramming