面试题库

全部 Django Elasticsearch FastAPI Flask Function Call JVM Java IO Java 后端 Java 基础 Java 并发 Java 新特性 Java 集合 JavaWeb Kafka LLM LangChain LangGraph MongoDB MyBatis MySQL Netty Nginx Python RAG RabbitMQ Redis RocketMQ Spring Spring AI SpringBoot SpringCloud SpringMVC ZooKeeper 分布式后端技术大厂面经大数据微服务提示词工程操作系统数据库数据结构智能体海康威视2026秋季社招专项消息队列监控运维算法系统设计缓存计算机基础计算机网络设计模式面试题高可用高并发

RAG 中等

Design a retrieval strategy for a RAG system that needs to handle both structured data (knowledge graphs) and unstructured data (text documents) simultaneously

作答 →

RAG 中等

How can fine-tuning embedding models improve the retriever’s performance in RAG?

作答 →

RAG 中等

What role does cosine similarity play in relevant chunk retrieval within a RAG pipeline?

作答 →

RAG 中等

What is the purpose of overlap during chunking in a RAG pipeline?

作答 →

RAG 中等

Why is re-ranking important in the RAG pipeline after initial document retrieval?

作答 →

RAG 中等

Explain the importance of chunking in RAG

作答 →

RAG 中等

Explain the steps in the indexing process in a RAG pipeline

作答 →

RAG 中等

Can you give examples of real-world applications where RAG systems have demonstrated value?

作答 →

RAG 中等

Explain the pros and cons of semantic chunking

作答 →

RAG 中等

What are the criteria to choose a specific chunking method in RAG?

作答 →

RAG 中等

What are some common chunking methods used in RAG?

作答 →

RAG 中等

What are the fundamental challenges of RAG systems?

作答 →