Komatsu RAG Documentation System
A retrieval-augmented generation system designed to make technical documentation easier to search, retrieve, and use through a full-stack AI experience.
- ↗ Built a RAG/LLM documentation system to reduce manual documentation search effort
- ↗ Developed scalable backend architecture with Flask and a React + TypeScript frontend
- ↗ Explored deployment with CyVerse infrastructure, NGINX, Gunicorn, and Linux VMs
- ↗ Strengthened experience in retrieval, source-grounded generation, and infrastructure
Overview
At Komatsu, I worked on a Retrieval-Augmented Generation system designed to streamline documentation workflows. The problem was familiar across many organizations: valuable knowledge existed, but finding the right information took too much manual effort.
The project connected AI retrieval, LLM response generation, backend architecture, frontend usability, and deployment exploration into one full-stack system.
The problem
Technical documentation is only useful when people can find the right answer at the right time. Traditional search often fails when users do not know the exact terminology, when documents are spread across sources, or when answers require context from multiple places.
The product goal was to reduce the friction between a question and a useful answer.
Users and stakeholders
The primary users were documentation consumers: engineers, operators, or team members who needed faster access to technical information. The broader stakeholder group included teams responsible for documentation quality, accuracy, and workflow efficiency.
My role
I worked as an SDE intern and AI systems builder. I contributed to the RAG architecture, backend development, React + TypeScript frontend integration, and deployment exploration using Linux infrastructure tools.
Product decisions
The most important product decision was to make the system source-grounded. A useful enterprise AI documentation tool cannot simply generate plausible answers. It has to retrieve relevant context, use that context to respond, and support user trust.
That meant thinking about:
- document ingestion
- retrieval quality
- query handling
- response grounding
- user interface clarity
- deployment constraints
Technical approach
The system used a Flask backend integrated with a React + TypeScript frontend. The AI workflow centered on retrieval-augmented generation with Llama-based language model capabilities. I also explored deployment using CyVerse infrastructure, NGINX, Gunicorn, and Linux VM workflows.
This gave me hands-on experience across the full stack: AI pipeline, API design, frontend integration, and production-style infrastructure.
Impact
The project was designed to reduce manual documentation effort and improve access to technical information. It gave me a strong foundation in how AI systems need to be built for real workflows, not just isolated demos.
What I learned
RAG systems are not just “chat with documents.” The hard parts are source quality, chunking, retrieval, evaluation, UX, and trust. A RAG product succeeds when users can ask better questions, get grounded answers, and understand why the answer is reliable.
PM / APM interview story
Situation: Technical documentation was time-consuming to search and difficult to use efficiently.
Task: Build a full-stack RAG/LLM system that could retrieve relevant documentation and generate helpful answers.
Action: I developed backend architecture with Flask, integrated a React + TypeScript frontend, worked with RAG/LLM workflows, and explored deployment using Linux VM, NGINX, and Gunicorn infrastructure.
Result: The project reduced documentation search friction and gave the team a scalable direction for AI-supported knowledge access.