Built a Retrieval-Augmented Generation (RAG) system to support customer-facing queries by grounding AI responses in internal documentation and knowledge bases. The system ingested structured and unstructured support content, generated embeddings, and retrieved relevant context at query time to produce accurate, consistent answers rather than generic model responses.
The focus was on building a simple, maintainable pipeline with clear data flow and traceability, making it easy to debug incorrect answers, update knowledge sources, and iteratively improve response quality as documentation evolved.
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Posted Jan 7, 2026
Customer Support AI Assistant (RAG Pipeline)
Built a Retrieval-Augmented Generation (RAG) system to support customer-facing queries by grounding AI responses...