Speaker
Description
This fourth hands-on tutorial connects retrieval and generation into an end-to-end RAG chain. Participants will start from a user question, retrieve relevant chunks, build a grounded prompt, call the selected language model and return an answer with source attribution. The notebook will expose each intermediate step so participants can see exactly what the model receives before generating a response. They will compare answers produced with and without retrieved context, highlighting the role of evidence in reducing hallucinations and improving factuality. The session also demonstrates how to handle insufficient evidence by producing cautious responses rather than unsupported claims. Participants will inspect whether cited sources actually support the generated answer and prepare the chain for a simple user-facing interface. By the end, participants will have a runnable RAG workflow that links retrieval, prompt construction, generation and transparent citations. This workflow provides practical foundations for later evaluation and deployment discussions in the webinar.
