A backend and systems engineer developed a retrieval-augmented generation (RAG) chatbot for their personal portfolio by writing the entire pipeline manually in TypeScript, rather than using a framework like LangChain. This approach allowed for greater control over the system's components, such as using hand-curated keywords for retrieval instead of opaque embeddings, making the data directly editable for corrections. The system is also designed as a parametrizable data-driven platform, supporting multiple languages and configurable assistant behaviors through an admin panel, and is deployed live on a virtual private server. AI
IMPACT Demonstrates a hands-on approach to building RAG systems, emphasizing control and debuggability over framework reliance.
RANK_REASON Developer describes building a custom RAG chatbot pipeline by hand, detailing technical choices and deployment.
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