04 Gen AI Interview Preparation: How to Design Production-Ready RAG Systems @KGPTalkie
04 Gen AI Interview Preparation: How to Design Production-Ready RAG Systems  @KGPTalkie
Uploaded March 2026 | Updated September 2026, 2 weeks ago
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πŸ“Ί Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1

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Most developers think RAG is simple β€” store embeddings in a vector database and call the LLM. But that is naive RAG, and it will never survive in production. In this video, we break down how to design a truly production-ready RAG system in a structured interview Q&A format, covering everything an AI Engineer or LLM Engineer is expected to know in system design interviews.

We walk through the five critical components of production RAG β€” hybrid search, metadata filtering, re-ranking, guardrails and validation, and caching strategy. We then compare naive RAG versus production RAG architecture step by step, and close with a three-test framework β€” Accuracy, Latency, and Cost β€” that helps you determine whether your RAG system is actually ready for production. A senior-level insight is included that demonstrates architectural maturity to interviewers.

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🧠 Master OpenAI Agent Builder - Deploy Chatbot to Your Website
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πŸš€ Agentic RAG with LangChain & LangGraph
udemy.com/course/agentic-rag-with-langchain-and-langgraph/?referralCode=C0BCC208F53AF2C98AC5

🧠 LangGraph with Ollama
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⚑ Ollama and LangChain
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πŸ”§ Fine-Tuning LLM with Hugging Face Transformers
udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/?referralCode=6DEB3BE17C2644422D8E

πŸ“– NLP with BERT in Python
udemy.com/course/nlp-with-bert-in-python/?referralCode=063516494616C76907CD

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04 Gen AI Interview Preparation: How to Design Production-Ready RAG Systems

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