For a while, I thought building an AI chatbot was simply about connecting an LLM to a chat interf...For a while, I thought building an AI chatbot was simply about connecting an LLM to a chat interf...
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Yinka Adesina
• Jul 25
For a while, I thought building an AI chatbot was simply about connecting an LLM to a chat interface.
As I started building one from scratch, I realized there was much more to it.
I set out to build an AI-powered customer support assistant that could answer questions based on a company's own documentation—not just generate responses from general knowledge.
That journey introduced me to Retrieval-Augmented Generation (RAG), vector embeddings, semantic search, MongoDB Atlas Vector Search, streaming responses, and the engineering challenges that come with building reliable AI systems.
One challenge stood out.
The chatbot kept responding with, "I don't have that information," even though the answers were clearly available in the knowledge base. After digging into the issue, I discovered the problem wasn't the AI model—it was the retrieval layer. My vector search wasn't returning any relevant documents, leaving the model with no context to work with.
That debugging session reinforced one lesson I'll carry into every AI project:
An LLM is only as good as the context you provide.
Building this project pushed me beyond simply integrating AI APIs. It challenged me to think about retrieval, context management, backend architecture, resilience, and creating an experience users can actually trust.
The project is now live, and I'd love for you to give it a try: 👉 https://passeduaiagent.vercel.app/
If you're interested in the technical architecture, implementation details, and the decisions behind the build, I've documented the entire project on my Contra portfolio.
Feedback is always welcome. If you're building AI products or exploring RAG systems, I'd be happy to connect and hear about what you're working on.
#AI #RAG #OpenAI #NextJS #MongoDB #TypeScript #SoftwareEngineering #FullStack #BuildInPublic
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