A complete collection of RAG interview questions, answers (286 questions & 18 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).
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。
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.
Learn Retrieval-Augmented Generation (RAG) from scratch by manually building chunking, embeddings, retrieval, reranking, answerability, local generation, Graph-RAG, and Self-RAG.
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.
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