This repository compiles extensive interview experiences and common questions for Large Language Model (LLM) application development roles, targeting engineers with practical experience. It aims to provide a realistic overview of the hiring landscape, common technical challenges, and effective preparation strategies, thereby increasing a candidate's chances of success in a competitive market.

How It Works

The resource aggregates firsthand interview insights from a candidate with five years of experience across numerous tech giants. It details prevalent questions covering LLM fundamentals, RAG, Agent development, system design, and traditional CS concepts, emphasizing practical application and hands-on experience over purely theoretical knowledge.

Highlighted Details

• Practical LLM skills, particularly fine-tuning and deployment, are highlighted as critical differentiators, often more so than deep theoretical knowledge.
• Key LLM application areas frequently probed include RAG techniques (chunking, retrieval), Agent design patterns (memory, function calls), and prompt engineering strategies (CoT, ReAct).
• Traditional computer science fundamentals ("八股文") remain relevant, covering topics like distributed systems, databases, and concurrency, even for specialized LLM roles.
• The resource details diverse application domains, from game development and customer service bots to enterprise solutions and personal assistants, providing context for role expectations.

Maintenance & Community

This repository appears to be a personal collection of interview notes and lacks information regarding active maintenance, community channels (e.g., Discord, Slack), or a public roadmap.

Licensing & Compatibility

No open-source license is specified within the provided README content. This absence necessitates caution regarding usage, modification, and distribution rights.

Limitations & Caveats

The content represents a single individual's interview journey and may not cover all possible scenarios or company requirements. Its focus is heavily skewed towards practical application experience, potentially overlooking deeper theoretical aspects some roles might demand. The lack of code examples or structured tutorials limits its utility as a hands-on learning tool. Furthermore, the absence of licensing information poses a significant adoption blocker.