> ## Documentation Index
> Fetch the complete documentation index at: https://docs.folksbase.joselito.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Using AI

> My philosophy and workflow

You can’t talk about software engineering today without addressing the neural network in the room.

Given how AI is fundamentally reshaping our industry, I felt it was essential to dedicate a specific section to how it influenced folksbase. For me, this isn't just about using a trendy tool; it's about my philosophy on where AI fits into a professional workflow and how it should—or shouldn't—show up in a product built for humans.

## Invisible AI in Product

I have a very clear take on AI: unless your product is fundamentally an "AI tool," the technology itself should remain invisible. It should be a silent facilitator, not the main character. In folksbase, I treat AI as a layer of high-level assistance that disappears into the background to respect the user's time.

You’ll find AI working in two [specific places here](features/ai-integration), though I never explicitly label it as such:

* Smart Column Mapping: When you upload a CSV, the system suggests which headers match our schema (like mapping "E-mail Address" to email).
* Humanized Summaries: The notification emails sent via Resend after an import aren't just a list of raw numbers; they are brief, human-sounding summaries of the results.

The user doesn't need to know there's a Large Language Model involved; they just need the system to "magically" understand their data. It’s about functionality over marketing jargon.

## My Engineering Workflow

As a developer, I don't let AI drive the car; it’s my high-performance co-pilot. My development process for this challenge was a deliberate "pass-the-baton" between different models and my own expertise:

* Strategic Planning (Claude Opus): I used the Claude app directly for the initial architecture and the "Master Plan". In my experience, Opus still handles high-level, complex reasoning and long-term planning better than any other model.
* Core Development and writing commits (Amazon Kiro): This was my "vibe coding" partner. Kiro handled the heavy lifting and micro-features, allowing me to focus on the high-level logic.
* Code Review & Analysis (Claude Sonnet): I used Sonnet specifically through Claude Code. It has demonstrated superior performance for CLI-based tasks and code analysis, plus the tokens are more cost-effective for the volume of code being reviewed.
* QA & Final Refinement (Amazon Kiro): The final stretch involved squashing bugs and polishing the UI to get it as close to "pixel perfect" as possible.
* Documentation & Context Refining (Gemini): For the final documentation and the "human" side of the project, I brought in Gemini. While other models handled the raw code, Gemini acted as my specialized co-pilot for the docs. Because it understands the full context of how I write, my personal preferences, and the specific soul I wanted to give to folksbase, it was the best tool to ensure that this documentation sounds exactly like a conversation with me—balancing technical depth with my authentic personality.

In the end, while the AI wrote a significant portion of the code, the "soul" of the project—the design decisions, the UX trade-offs, and the final polish—came from somenohe who truly loves building for the web.