RAG and knowledge-grounded chatbot experimentation

Purpose. This work explores how a chatbot can answer from a defined knowledge source rather than relying only on a general model.

Vinod’s contribution. As part of current AI engineering training, Vinod designed and ran the experiments: preparing source material, testing retrieval-grounded answers, and reviewing whether responses stayed aligned with the retrieved text.

What was practised. Embeddings, vector-database concepts, retrieval-augmented generation, knowledge-grounded responses, and evaluation considerations — whether an answer is supported by the retrieved source.

Tools used. RAG implementation, embeddings, vector databases and knowledge-grounded chatbot techniques, as listed in the professional toolkit, within the Be10x AI Engineering Career Accelerator — Inner Circle programme, which covers embeddings, vector databases and retrieval-augmented generation.

Status and evidence. This is current learning and practical experimentation, not a production system and not a paid client delivery. No public repository, screenshot, evaluation score or deployment URL is published here.