Learn AI, LLMs & RAG by Building Real Projects — One Step at a Time

How would you reduce hallucination in a RAG pipeline?

Tighter chunking, hybrid search + reranking, and a Guardrails layer that checks answers against source.

Design a multi-agent system for customer support.

An orchestrator delegates to narrow specialist agents, with explicit failure handling.

How do you evaluate RAG answer quality?

Faithfulness, relevancy, and context recall — scored against a golden dataset, not eyeballed.

How do you prevent prompt injection in production?

Guardrails for input filtering, plus treating retrieved content as untrusted data.

Pay-per-use vs. provisioned throughput — when which?

Pay-per-use for unpredictable traffic; provisioned once volume and latency needs are steady.

Explain your capstone's architecture.

You built it — so you walk through the real diagram, not a whiteboard guess.