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Engineer builds custom RAG chatbot pipeline by hand, bypassing frameworks

By PulseAugur Editorial · [1 sources] ·

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.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Engineer builds custom RAG chatbot pipeline by hand, bypassing frameworks

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Developer describes building a custom RAG chatbot pipeline by hand, detailing technical choices and deployment.
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  1. dev.to — LLM tag TIER_1 English(EN) · Brian ·

    Everyone builds a RAG chatbot with LangChain. I wrote the pipeline by hand — and deployed it

    <blockquote> <p>The "personal AI that knows my projects" is a whole genre now. Here's the version I built, and the two choices that took it somewhere different.</p> </blockquote> <p>The standard recipe is everywhere: LangChain, a vector database, a local model, and you've got an …

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