#面试
5 related articles
·4 min
A Complete Breakdown of the Engineering Challenges Behind RAG and Agent Interview Questions
Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.
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·6 min
Getting Started with LangChain: A Deep Dive into Model and Agent Core Concepts
Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.
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·3 min
Python Excel Automation in Practice: Data Filtering and Categorization with Pandas
Learn how to use Python Pandas to automate Excel data filtering and categorization. Core code is just 6-8 lines — handle massive datasets effortlessly.
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Tutorials·3 min
Java Developers Transitioning to AI Engineers: New Interview Trends and Learning Roadmap
Deep analysis of interview trends for Java developers transitioning to AI engineers, covering LLM integration, RAG, Spring AI framework practice, with a complete learning roadmap.
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Tutorials·1 min
Ningbo Bank AI Agent Interview Questions: Reasoning Optimization + Agent Debugging + Python Key Topics
Deep analysis of real Ningbo Bank AI Agent interview questions covering LLM multi-path reasoning optimization, agent debugging methodology, Python deep/shallow copy, GIL, and decorators.
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