The article explains how LLM‑driven agent tool calls differ from traditional function calls in timing, parameter sourcing, error handling, call‑chain observability, and performance, and it provides concrete examples, failure modes, and interview‑ready summaries.
This extensive interview guide covers 22 core RAG questions, detailing the definition, workflow, embedding selection, vector database choices, retrieval optimization, multi‑turn handling, context compression, evaluation metrics, knowledge‑graph integration, operational challenges, Agentic and hybrid RAG, document update strategies, similarity algorithms, and hallucination mitigation, providing concrete examples and practical advice for AI interview preparation.
The article explains that multi-agent systems typically operate in three patterns—sequential execution, parallel execution, and an evaluator-optimizer loop—covers when each pattern is appropriate, and offers interview tips on how to discuss these designs effectively.
This guide breaks down the four major large‑model training paradigms—pre‑training, supervised fine‑tuning, preference alignment, and RLHF—explaining which parameters are updated, how attention is reshaped, and what capabilities are gained, so you can deliver a structured, interview‑ready answer.
This article breaks down the core of large‑model training by showing that training optimizes neural‑network parameters, that attention is a mechanism realized by those parameters, and that knowledge is encoded implicitly within the weight matrices, providing a clear hierarchy for interview or presentation use.
RAG (Retrieval‑Augmented Generation) is a hot interview topic, and many candidates stumble on semantic‑loss issues; this article dissects a real JD interview case, identifies three core shortcomings, and presents a three‑step technical solution—structure restoration, semantic splitting, and hybrid retrieval—plus a ready‑to‑use answer template.
This article explains why building a high‑quality RAG knowledge base is critical, outlines offline parsing techniques for multi‑format documents, presents semantic chunking strategies that preserve structure and context, and shows how to answer interview questions with a robust, production‑ready pipeline.
This article explains the fundamental purpose, architecture, and variants of the Transformer model—including Encoder‑Decoder, Encoder‑only, and Decoder‑only designs—while detailing how attention mechanisms work and why modern large‑language models favor the Decoder‑only approach, providing a concise framework for answering interview questions.
The article examines Meituan's AI-powered interviewers and the emerging "Vibe Coding" approach, arguing that future hiring will prioritize developers who can collaborate with AI tools rather than merely memorizing algorithms, and outlines new interview expectations for junior, mid‑level, and senior engineers.
The article examines Meituan’s pioneering use of AI interviewers and the NoCode product, explains the emerging “Vibe Coding” approach, and argues that future software engineering interviews should assess candidates’ ability to collaborate with AI tools rather than merely memorizing algorithms.
The article explains the technical background and suitable scenarios for Retrieval‑Augmented Generation (RAG) and model fine‑tuning, compares their strengths, discusses how they can be combined, and provides interview‑style Q&A on their capabilities, risks, and differences from model distillation.
This article provides a step‑by‑step technical guide to optimizing Retrieval‑Augmented Generation (RAG) for interview scenarios, covering query rewriting, HyDE, fallback strategies, routing and prompt routing, multi‑representation indexing, hybrid retrieval, re‑ranking, self‑RAG, generation control, performance benchmarking, and a practical checklist with concrete code examples and metrics.
This case study details how 58’s UX team researched and iterated an AI interview product for blue‑collar job seekers, uncovering user needs, testing visual and interaction elements, and translating insights into concrete design guidelines.
This article presents over twenty essential Retrieval‑Augmented Generation (RAG) interview questions with detailed answers, covering fundamentals, applications, architecture, training, limitations, ethical considerations, and integration, offering AI enthusiasts and job candidates a comprehensive guide to mastering RAG concepts.
Meituan now uses an AI‑driven interview on the Niuke platform that starts with language selection, presents six technical questions (with three chances to swap), followed by two general questions, each timed and often followed by rapid follow‑ups, giving candidates a glimpse of the rigid, fast‑paced assessment format.
This article explains how a lightweight, intuitive design language and an AI‑driven asynchronous interview feature were created to ease candidate anxiety, streamline recruiter workflows, and achieve high user satisfaction across millions of interview sessions.
This guide shares practical advice from multiple AI interview experiences, covering how to build a standout profile, a curated list of target companies, interview techniques, motivation for meaningful work, and essential computer science, math, and machine‑learning fundamentals to help graduates secure AI roles.
