Two Years of AI-Assisted Code Development. Here Is What I Learned and Built.

Two years ago, when AI coding tools became mainstream, I made a decision.

I would show up every day and build.

Not just as experiment. Build. Ship. Deploy real full-stack applications.

Using Lovable, GitHub Copilot, v0, Codex, and every AI platform that could accelerate development. Eventually I built more than 50 fully working web applications. All deployed to cloud. Every one of them followed a disciplined delivery process — SDLC from development to deployment, automation testing, CI/CD workflows, and documentation thorough enough for anyone to pick up where I left off.

I wanted to build an Blogging platform to publish my work and knowledge, I'be gather over the years. So I used Lovable to build my blogging product and it is the latest project in that full-stack development journey.

And it is the one that brings everything I learned together.

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What two years of building taught me

The biggest lesson was not about the tools. But it is applying the SDLC process even in AI developed products.

Most developers use AI to generate code. They paste a problem, get an output, move on. The result works — sometimes. But it is not designed. It is not secure by default. It has no delivery process behind it. And it does not hold up when it needs to.

The developers who build things that last do something different. They design before they prompt.

That means: before writing a single prompt, define the architecture. Map the component boundaries. Define the data flow. Specify the security requirements. Decide what the system needs to do before you ask the AI to build it.

For three-tier applications, this approach reduced AI credit and token usage significantly and produced cleaner, more consistent outputs. Context engineering — giving the AI the right constraints upfront — matters as much as the prompt itself.

This is not theory. It is what I observed across 50+ builds.

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Applying that methodology to the blogging platform product

When I decided to build the product - gsaravanan.com, I had a choice to simply use Lovable to build a product in single prompt.

I could open Lovable and type "build me a blog." That would have produced something. But it would not have been what I needed.

Instead, I designed it first.

Here is what I needed before I wrote a prompt:

• Blog sections organised by topic so readers can filter by interest
• Markdown-based content — no CMS database for articles
• Database only for subscriber emails
• Migration path for existing content from gsaravanan.dev
• README that explains exactly how to add new content
• GitHub Actions CI that validates every new markdown file before publishing
• Admin endpoint to manage content
• Secure by default — latest packages, no vulnerable or deprecated dependencies
• Modern, CMS-like experience

That is the design. Then I wrote the prompt.

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The prompt I used

let us create a blogging website for saravanan (https://gsaravanan.dev) -
tech advisor and consultant - who specialises in devops, secops, cloud,
ai product development, ai-sdlc practices, effective ai code development,
ai engineering, process first sdlc automation using devops process automation.

the blog site should have sections for various topics, users should be able
to access blogs based on their interest. migrate some of the blogs from
gsaravanan.dev here.

use ai code development best practice, and add markdown to track all the
requirements and roadmap, prioritise secure dev practice, and use latest
packages with no vulnerable or deprecated packages. think modern and
content management system like website.

Add repo project readme explains the repo and how to add content.

Add GitHub action workflow that finds the new blog md file in blog directory
and checks for necessary blocks and information that is needed to publish
the blog, including seo check and keywords.

Add admin endpoint to view content list and manage.

Look at what is in that prompt.

The persona and specialisations are in the first message. Lovable knows who the site is for and what it covers. That shapes every design, copy, and navigation decision it makes.

Security requirements are constraints from the start — not a review step added later. "No vulnerable or deprecated packages" is in the first message.

The GitHub Actions CI workflow is a requirement, not a nice-to-have. Every article I write now goes through an automated check before it goes live. This is DevOps applied to content. The same engineering discipline I use for application delivery, applied to publishing.

That is the methodology in action.

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I've done SEO without being an SEO expert

I had no prior SEO experience when I applied SEO to this product.

I followed Lovable's SEO enhancement guidance step by step. Meta descriptions, Open Graph images, structured data, canonical URLs, Sitemap — all handled through Lovable's built-in tooling.

I used Lovable to generate the favicon and OG image directly. No separate design tool. No Figma. One tool, end to end.

Then I connected to Google Search Console through Lovable's third-party integrations, verified site ownership, and submitted the sitemap.

The Page speed result after the first deploy:

Metric	Mobile	Desktop
Overall Score	86%	94%

Desktop at 94% is strong. These are solid scores for a newly launched blog built without any manual performance optimization.

The right tool like Lovable, used correctly, closes the knowledge gap.

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The architecture that makes it production-grade

I used Markdown for publishing the Blog content.

Markdown content means no database reads for page loads. The site is fast because the content is static. Every article is a .md file in a directory. That means content is portable, easy to manage, and not locked into any platform.

The database only handles: subscriber emails. That is the right separation of concerns.

The GitHub Actions workflow validates every new markdown file before it publishes. It checks:

• Required frontmatter fields are present
• SEO fields are defined
• Keywords are set
• Content structure matches the expected format

That is a content quality gate built into the publishing pipeline. It works the same way a deployment pipeline works for code. You do not push untested code. You do not publish unvalidated content.

The README explains exactly how to add a new article. Anyone can pick it up and contribute without needing to ask how it works. That is documentation-first development — the same standard I apply to every project I build.

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How Building Apps in Lovable helped in client work

I have helped clients who built products with Lovable and got locked in.

The most common scenario: they built something that works, it is on Lovable Cloud, but they have no GitHub integration, no release process, and no path to production.

The fix follows the solution to Detach the application from Lovable Cloud. Integrate with GitHub. Set up a release process. Migrate the database to managed DB infrastructure. Add the engineering discipline the original build.

That process is not complicated. But it requires understanding how AI-built applications work — their structure, their gaps, and what production readiness actually demands.

Two years of building and deploying those applications is what gives me that understanding of how the code build works in AI code dev tools.

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Key lessons

Design before you prompt. If you cannot describe what you need clearly, you have not designed it yet. Time spent on design comes back in build quality.

Security requirements go in the first message. Not in a review. Not in a later sprint. In the prompt. The system is built to the constraints it is given.

CI is for content as much as it is for code. Automated validation before publishing is the same principle as automated testing before deployment. Apply it everywhere.

You do not need to be an expert in every area. I was not an SEO expert. I followed Lovable's guidance and got an 86% mobile score on first deploy. The right tool, used correctly, is enough to get a production-grade result.

The methodology generalises. Everything I describe here — design first, secure by default, CI gates, documentation — applies to every application I build. It is not blog-specific. It is how I approach every project.

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Closing thoughts

This blog is the proof of what can be done using structures process knowledge applied to AI based code development.

I write about using AI tools to build production-grade systems. This site is an AI-built, production-grade system. The PageSpeed scores are real. The GitHub Actions CI is running. The sitemap is in Google Search Console.

The gap between using AI and using it well is not about which model you use or which tool you pick. It is about what you do before you open the tool. Design the system. Define the constraints. Know what you are building and why.

Build that foundation and the AI tools do what they are best at — moving fast on a clear brief.

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Tools used in this build

• Lovable — full-stack application development, favicon, OG image, meta descriptions, SEO enhancements
• GitHub Actions — CI pipeline for content validation
• Google Search Console — indexing and search performance
• PageSpeed Insights — performance measurement