A complete collection of RAG interview questions, answers (200 questions & 12 RAG types), system design scenarios, architecture patterns, and production-ready concepts.
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Updated
Jun 7, 2026
A complete collection of RAG interview questions, answers (200 questions & 12 RAG types), system design scenarios, architecture patterns, and production-ready concepts.
Training code for advanced RAG techniques - Adaptive-RAG, Corrective RAG, RQ-RAG, Self-RAG, Agentic RAG, and ReZero. Reproduces paper methodologies to fine-tune LLMs via SFT and GRPO for adaptive retrieval, corrective evaluation, query refinement, self-reflection, and agentic search behaviors.
Self-Reflective Question Answering for Biomedical Reasoning. GRPO fine-tuning via QLoRA & Unsloth with rewards for correctness, relevance, groundness, utility & XML structure. Structured think → answer → self-reflection with context grading, relevance assessment & groundness evaluation. DeepEval LLM-as-a-Judge (GEval, Faithfulness, Relevancy).
Evidence-synthesis RAG assistant for TCM practitioners — hybrid vector + knowledge graph retrieval over 17 classical texts, with query classification, self-critique verification, and blind A/B arena evaluation.
生产级 3GPP 5G 规范 RAG Agent:自然语言提问,回答带段落级原文引用 + 严格 grounding,覆盖 Rel-18/19 全部 TS。
Production-ready Retrieval-Augmented Generation (RAG) system with hybrid retrieval, Self-RAG agent workflows, cross-encoder reranking, and comprehensive benchmarking.
AutoDocThinker is a production-ready Agentic RAG system that ingests PDFs, DOCX, URLs, and raw text into a Hybrid Search index (ChromaDB + BM25 + RRF + CrossEncoder), then answers natural language queries through four selectable LangGraph workflows — Naive, Advanced, CRAG, and Self-RAG.
Advanced RAG with hybrid search, query classification, answer fusion, and self-correction
Agentic RAG Multi-Agent Exam Tutor — LangGraph multi-agent system for Marine Structures | DeepSeek V4 Pro | BGE-M3 | ChromaDB | Self-RAG | FastAPI
Advanced RAG Q&A for PDFs. Delivers structured, educational answers with diagrams & follow-ups via Streamlit. Powered by LangGraph, featuring hybrid retrieval, cross-encoder reranking, and Self-RAG verification using Groq Llama 3.3 70B & local Ollama embeddings. Includes persistent chat & semantic search.
A progressive learning lab implementing 8 RAG pipelines from scratch to Agentic, Self-Correcting, and Evaluated RAG using LangChain, LangGraph, CRAG, & RAGAS.
Advanced RAG using langgraph which uses websearch functionality to produce relevant documents.
A Streamlit-based Self-RAG-inspired evidence QA assistant with retrieval, evidence critique, answer revision, and reflection.
Production adapters and pipelines for PortfolioCore. Vector stores (pgvector, Qdrant), graph stores (Neo4j), embedders (OpenAI), Broadway pipelines, advanced RAG (Self-RAG, CRAG, GraphRAG, Agentic), multi-graph federation, and observability.
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