Developer Stories: From Web Development to AI — Josaphat’s Journey
An interview by CoderLegion
The world of software development is changing quickly. AI-assisted coding is making it easier than ever to build applications, experiment with new technologies, and turn ideas into working prototypes.
But there is another side to this transformation: are developers still learning deeply when AI can write so much of the code for them?
For G-Josaphat E. Tagba, known on CoderLegion as @Josaphatstar, this is a question he has experienced firsthand.
Josaphat is a web developer with a growing focus on artificial intelligence and data. Alongside his web development journey, he is pursuing a Master's degree in AI & Big Data, exploring areas including machine learning, data pipelines, statistics, and modern AI technologies.
His recent work reflects that combination of web development and AI. One of his projects involved building a local RAG assistant using Ollama, ChromaDB, LangChain, FastAPI, and Streamlit. The system was designed to help technicians search through thousands of pages of technical documentation using natural language while keeping sensitive information inside the local infrastructure.
We invited Josaphat to share his journey with the CoderLegion community—from getting started with web development and AI to discovering the hidden challenges of AI-assisted coding and building a real-world local RAG system.
1. From Web Development to AI
CoderLegion: You describe yourself as a web developer while also pursuing a Master's in AI & Big Data. How did your journey develop from frontend/web development into AI, and what made you want to explore both areas?
Josaphat:
It didn't start as a plan, it happened in parallel.
I started getting into web development on my own. I liked the immediate feedback of building things you could see and interact with.
At the same time, I enrolled in a Master's program in AI & Big Data, which took me in a completely different direction—machine learning, data pipelines, statistics, etc.
2. Learning to Code in the AI Era
CoderLegion: You've written about how learning with AI can make development much faster while sometimes making it harder to develop deep understanding. When did you first notice this yourself, and how has it changed the way you learn?
Josaphat:
I noticed it during a Nuxt project I built with heavy AI assistance.
When I finished, someone asked me to explain some of the decisions I'd made in my own code. And I struggled.
Not because the code was wrong, but because I hadn't really wrestled with it. I'd asked AI, got an answer, moved on.
That was the moment I realized there's a difference between moving fast and actually understanding.
Before AI was this capable, I had to sit with problems. That frustration was uncomfortable, but it was also doing something.
Every bug I fixed alone left a mark.
Now I can skip that uncomfortable part entirely, and that's both the power and the trap.
3. Building Your Local RAG Assistant
CoderLegion: You recently built a local RAG assistant using Ollama, ChromaDB, LangChain, FastAPI and Streamlit to help technicians search through thousands of pages of technical documentation.
What problem were you trying to solve, and what was the biggest challenge you encountered while building it?
Josaphat:
The idea was to build an AI assistant that could answer technical questions by searching through that documentation intelligently, in natural language.
The biggest challenge was the scale of the document ingestion.
We were working with around 10 PDFs totaling roughly 2,000 pages—about 10,000 vectors once processed into ChromaDB.
Getting that pipeline to work reliably, with consistent retrieval quality, took a lot of iteration.
The second challenge was debugging things I'd never built before. LangChain's API had changed significantly between versions, ChromaDB had breaking changes between v1 and v2, and LangGraph was new territory.
4. Why Local AI?
CoderLegion: One of the constraints of that project was that the data could not leave the local infrastructure, meaning you couldn't simply send the documents to OpenAI or another cloud AI provider.
How did that constraint influence your architecture, and what did you learn from building an AI system entirely locally?
Josaphat:
The constraint was non-negotiable: the documents contained sensitive technical data that couldn't leave the local infrastructure.
Cloud APIs were off the table.
Everything had to run locally.
5. When Things Break
CoderLegion: Your RAG project involved several real problems—from ChromaDB connectivity and changing LangChain modules to Llama 3 performance on CPU.
Which problem taught you the most, and what did it change about the way you approach debugging?
Josaphat:
The problem that taught me the most was the ChromaDB API change.
The ChromaDB client interface changed significantly between versions. Code that had worked stopped working, with errors that weren't immediately obvious.
Combined with LangChain deprecating several imports that needed to be migrated to langchain_core, it forced me to dig deeper into what was actually happening rather than simply assuming the code should work.
What Comes Next?
Josaphat's story doesn't end with building a traditional RAG pipeline.
He is also exploring the next stage of AI application development: agentic AI.
In the next part of our conversation, we'll explore his move from RAG toward agentic architectures, what real-world projects have taught him that tutorials couldn't, his experience working with clients, and what he believes developers should focus on as AI becomes increasingly capable.
About Josaphat
G-Josaphat E. Tagba (@Josaphatstar) is a web developer and Master's student in AI & Big Data, with interests spanning web development, artificial intelligence, machine learning, data, and AI-powered applications.
His recent work includes experimenting with RAG systems, local LLMs, AI tooling, and modern web technologies.
You can connect with Josaphat on CoderLegion:
https://coderlegion.com/user/Josaphatstar
This interview is part of CoderLegion's Developer Stories series, where developers share their real experiences, challenges, projects, lessons, and career journeys with the community.
