[GitHub Global] Translate Vibe Coding 零基础教程/40 编程学习/06 AI 应用开发面试题.md to en
This commit is contained in:
yupi-translate-app[bot]
committed by
GitHub
GitHub
parent
f39b1dd65f
commit
f7c1224d69
@@ -0,0 +1,301 @@
|
||||
# Must-Know for AI Application Development Interviews
|
||||
|
||||
> AI development is not just about calling an API
|
||||
|
||||
|
||||
|
||||
Hello, I'm programmer Yupi.
|
||||
|
||||
Due to the popularity of AI, many companies have started AI-related businesses or added AI features to existing projects. This has also created a new vertical position for development-oriented programmers — AI application development.
|
||||
|
||||
But some friends might think: "AI application development? Isn't it just about calling an API? What's so difficult about it?"
|
||||
|
||||
|
||||
|
||||
This really proves that saying — the less you know, the more you think you know.
|
||||
|
||||
It's like someone asking: How does an e-commerce system push products you like to the homepage?
|
||||
|
||||
Some students immediately answer: Isn't it just about recommendation algorithms?
|
||||
|
||||
Indeed, but these four words might represent the result of many elites working day and night, continuously researching and optimizing to present to users.
|
||||
|
||||
The same goes for AI application development. Calling an API can indeed fulfill some requirements, but when delving into specific business scenarios and solutions, there's still a lot of knowledge and experience worth learning.
|
||||
|
||||
Not long ago, I conducted a live interview with a Java backend developer with 3 years of experience, targeting an AI application development position. Below is the interview process. After reading it, you'll understand that AI development is far from just calling an API.
|
||||
|
||||
⭐️ Recommended video version: https://bilibili.com/video/BV1qgHezFEaR
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## I. Real Interview Case
|
||||
|
||||
### Candidate Background
|
||||
|
||||
Xiao Wang graduated in 2022 with over 3 years of Java backend development experience. At his previous company, he was responsible for building an electronic contract cloud platform, including core modules like the account system, permission system, and messaging system.
|
||||
|
||||
Besides traditional Java business, he also self-studied AI technologies for over half a year, working on projects like an electronic contract AI assistant (RAG system) and a mock interviewer Agent application. His tech stack includes Spring Boot, MySQL, Redis, RabbitMQ, etc., and he's familiar with prompt engineering, tool calling, Agents, etc., in AI.
|
||||
|
||||
**Target Salary: 20K**
|
||||
|
||||
Seems like a decent background, right? Let's see what the interview covered~
|
||||
|
||||
|
||||
|
||||
### Round 1: Prompt Engineering
|
||||
|
||||
Interviewer: Tell me about prompt engineering, preferably with examples from your projects. What techniques do you use to optimize prompts?
|
||||
|
||||
Xiao Wang's Answer:
|
||||
|
||||
Prompt engineering is a crucial technique for improving the output quality of large models. Common techniques include:
|
||||
|
||||
1. Role Setting: Assign system prompts to large models, including role descriptions, tasks, and constraints.
|
||||
2. Few-shot Prompting: Provide the model with input-output examples for it to emulate.
|
||||
3. Chain of Thought: Have the model think before outputting answers.
|
||||
|
||||
In actual development, prompts need continuous iteration and optimization. Platforms like Alibaba Cloud Bailian can be used for A/B testing.
|
||||
|
||||
**Review**: This answer is somewhat comprehensive but lacks depth. Real prompt engineering goes far beyond these basic techniques.
|
||||
|
||||
|
||||
|
||||
### Round 2: AI Application Development Focus Areas
|
||||
|
||||
Interviewer: What are the key considerations when developing AI projects? What do you focus on?
|
||||
|
||||
Xiao Wang's Answer:
|
||||
1. Business Understanding: Deeply understand the business and abstract it into workflows or Agents.
|
||||
2. Engineering Optimization: Cache frequently asked questions, enable streaming output, and use different models for different task scenarios.
|
||||
|
||||
Interviewer Follow-up: Don't you focus on AI observability in your projects? What about AI accuracy and hallucination issues?
|
||||
|
||||
Xiao Wang: For accuracy, it can be optimized through prompts and RAG...
|
||||
|
||||
**Review**: This exposed a problem—knowing what to do but lacking production-level engineering experience.
|
||||
|
||||
|
||||
|
||||
### Round 3: Mitigating AI Hallucinations
|
||||
|
||||
Interviewer: How do you minimize hallucinations in AI calls during development?
|
||||
|
||||
Xiao Wang's Answer:
|
||||
1. Prompt Optimization: Clearer role definitions and added constraints.
|
||||
2. RAG System: Attach a knowledge base for the AI to base its answers on.
|
||||
3. Model Fine-tuning: Fine-tune models for specific domains.
|
||||
|
||||
Interviewer: Anything else? You've worked with tool calling—how do you mitigate hallucinations in tool calls?
|
||||
|
||||
Xiao Wang: What exactly do you mean by tool calling hallucinations?
|
||||
|
||||
Interviewer: For example, the AI calls a tool that doesn't exist in the system. How do you handle that?
|
||||
|
||||
Xiao Wang: ... (Silence is tonight's Cambridge Bridge)
|
||||
|
||||
|
||||
|
||||
Review: In reality, there are many engineering methods to handle tool calling hallucinations, such as adding hallucination handling strategies, adjusting large model parameters, prompt optimization, exception catching, etc.
|
||||
|
||||
|
||||
|
||||
### Round 4: Technical Framework Depth
|
||||
|
||||
Interviewer: What frameworks do you use for AI application development?
|
||||
|
||||
Xiao Wang: Spring AI.
|
||||
|
||||
Interviewer: What are the features of Spring AI?
|
||||
|
||||
Xiao Wang's Answer:
|
||||
1. Advisor Mechanism: Acts like an interceptor, allowing pre- and post-model call interception.
|
||||
2. Conversation Memory: Provides multiple built-in conversation memory implementations.
|
||||
3. Vector Storage: Built-in vector storage with options for custom implementations.
|
||||
4. ChatClient: A client for interacting with large models.
|
||||
5. Tool Calling: Converts Java APIs into tools via annotations.
|
||||
6. Structured Output: Specifies JSON format for responses.
|
||||
|
||||
Although Xiao Wang listed several features, his response was slow, and many features were left out.
|
||||
|
||||
Review: Seems like he's not very proficient.
|
||||
|
||||
|
||||
|
||||
### Interview Outcome and Summary
|
||||
|
||||
From the interview, Xiao Wang's strengths include:
|
||||
|
||||
- Practical experience in AI application development.
|
||||
- Decent grasp of basic concepts.
|
||||
|
||||
Weaknesses:
|
||||
1. Slow Response Pace: Needed step-by-step guidance from the interviewer, lacked initiative.
|
||||
2. Lack of Production-Level Practice: Knew what to do but not how to optimize.
|
||||
3. Weak Engineering Skills: Limited understanding of AI application monitoring, observability, and exception handling.
|
||||
|
||||
Ultimately, I think Xiao Wang has a shot at a 20K monthly salary, but it's not guaranteed. He needs to improve his engineering practice and communication skills.
|
||||
|
||||
|
||||
|
||||
## II. What Does AI Development Require?
|
||||
|
||||
From this interview, you can see that AI application development is far from just calling an API.
|
||||
|
||||
A qualified AI application developer needs to master:
|
||||
|
||||
|
||||
### 1. Prompt Engineering
|
||||
|
||||
- Role setting, few-shot learning, chain of thought.
|
||||
- Prompt optimization and A/B testing.
|
||||
- Prompt strategies for different scenarios.
|
||||
|
||||
|
||||
### 2. AI Engineering Capabilities
|
||||
|
||||
- Performance optimization (caching, streaming output, async processing).
|
||||
- Cost control (model selection, batch processing, load balancing).
|
||||
- Observability (monitoring, logging, metrics).
|
||||
- Exception handling and fault tolerance.
|
||||
|
||||
|
||||
### 3. Core Tech Stack
|
||||
|
||||
- RAG system design and optimization.
|
||||
- Vector database usage.
|
||||
- Hybrid retrieval strategies.
|
||||
- Model fine-tuning and evaluation.
|
||||
|
||||
|
||||
### 4. Frameworks and Tools
|
||||
|
||||
- Development frameworks like Spring AI, LangChain4j.
|
||||
- MCP model context protocol.
|
||||
- Various AI development tools and platforms.
|
||||
|
||||
|
||||
### 5. Business Understanding
|
||||
|
||||
- Abstracting complex business into AI workflows.
|
||||
- Agent design and multi-tool coordination.
|
||||
- User experience optimization.
|
||||
|
||||
|
||||
|
||||
## III. Recommended Interview Questions
|
||||
|
||||
To stand out in AI application development interviews, besides mastering the above knowledge, you should also practice interview questions.
|
||||
|
||||
|
||||
### Interview Duck AI Large Model Question Bank
|
||||
|
||||
We've compiled an **AI Large Model Question Bank** on [Interview Duck](https://www.mianshiya.com), featuring hundreds of selected questions covering:
|
||||
|
||||
- Fundamentals of AI large models.
|
||||
- Prompt engineering techniques.
|
||||
- RAG retrieval-augmented generation.
|
||||
- AI development frameworks (Spring AI, LangChain4j).
|
||||
- Vector databases and embeddings.
|
||||
- AI application development in practice.
|
||||
- Tool calling and MCP.
|
||||
- Agent design and optimization.
|
||||
|
||||
Question Bank Link: https://www.mianshiya.com/bank/1906189461556076546
|
||||
|
||||
|
||||
|
||||
Each question comes with detailed reference answers and knowledge tags to help you systematically prepare for interviews.
|
||||
|
||||
|
||||
|
||||
### FaceMore AI Mock Interviews
|
||||
|
||||
Besides practicing questions, mock interviews are crucial. Our [FaceMore](https://ai.mianshiya.com) offers **1v1 AI mock interviews**.
|
||||
|
||||
Access Link: https://ai.mianshiya.com
|
||||
|
||||
Features of FaceMore:
|
||||
|
||||
- Immersive Comprehensive Interviews: Customized questions based on your resume and target position, with 60+ minutes of 1v1 voice interview practice.
|
||||
- Specialized Interviews: 400+ interview directions to choose from for targeted improvement.
|
||||
- Resume Prediction: Predicts questions interviewers might ask based on your resume.
|
||||
- Detailed Review Reports: Evaluates your performance across multiple dimensions and suggests improvements.
|
||||
|
||||
|
||||
|
||||
New User Bonus: Register to receive 200 energy points, which can be used for 1 free immersive comprehensive interview or 1 specialized interview + 1 resume prediction.
|
||||
|
||||
Through repeated practice, you can:
|
||||
|
||||
- Familiarize yourself with the interview process and reduce nervousness.
|
||||
- Identify your weak areas.
|
||||
- Improve communication and logical thinking.
|
||||
- Enhance on-the-spot adaptability.
|
||||
|
||||
|
||||
|
||||
## IV. Learning Recommendations
|
||||
|
||||
Finally, here are some suggestions for those looking to transition into AI application development:
|
||||
|
||||
|
||||
### 1. Don't Stop at "It Works"
|
||||
|
||||
Many friends learn to call OpenAI's API and think they've mastered AI development. But real AI application development is about making applications run **stably, efficiently, and accurately** in production at **lower costs**.
|
||||
|
||||
|
||||
### 2. Prioritize Engineering Practice
|
||||
|
||||
Learn to use AI development frameworks instead of just writing raw HTTP requests. Also, understand AI application monitoring and observability, master cost and performance optimization techniques, and learn to handle various AI application exceptions.
|
||||
|
||||
|
||||
### 3. Deep Dive into Core Concepts
|
||||
|
||||
For example, prompt engineering isn't just about writing a few examples. RAG systems involve information retrieval, vector databases, re-ranking, and more—each with its own optimization techniques.
|
||||
|
||||
But I think the most complex part is Agent design, which involves tool selection, task decomposition, result integration, multi-agent collaboration models, etc.
|
||||
|
||||
|
||||
### 4. Work on More Projects and Summarize
|
||||
|
||||
This might sound like a cliché, but everyone knows that more projects mean more experience. Especially in AI application development, different business scenarios require customized optimizations for AI-generated results—it's not something you can solve by memorizing methodologies.
|
||||
|
||||
I've open-sourced many AI application development projects and even written several systematic tutorials to share with everyone:
|
||||
|
||||
- AI Application Generation Platform: https://github.com/liyupi/yu-ai-code-mother
|
||||
- AI Super Agent: https://github.com/liyupi/yu-ai-agent
|
||||
- AI Literature Reading Assistant: https://github.com/liyupi/literature-assistant
|
||||
- AI Knowledge Base: https://github.com/liyupi/ai-guide
|
||||
|
||||
On [Code Navigation](https://www.codefather.cn), I've also led multiple AI project tutorials covering almost all AI development technologies.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Final Thoughts
|
||||
|
||||
AI technology evolves rapidly, and the demands on programmers are constantly increasing. **AI knowledge is no longer just for algorithm engineers—it's a fundamental skill every programmer must master**.
|
||||
|
||||
Whether you're a frontend, backend, or full-stack developer, you need to understand the basics and practical methods of AI application development.
|
||||
|
||||
Because in the future of software development, AI will be everywhere.
|
||||
|
||||
Head over to [Interview Duck](https://www.mianshiya.com/bank/1906189461556076546) to practice questions and [FaceMore](https://ai.mianshiya.com) for mock interviews to prepare for your AI development journey!
|
||||
|
||||
|
||||
|
||||
|
||||
## Recommended Resources
|
||||
|
||||
1) Yupi AI Navigation Site: [Comprehensive AI resources, latest AI news, free AI tutorials](https://ai.codefather.cn)
|
||||
|
||||
2) Code Navigation Learning Circle: [Learning paths, programming tutorials, practical projects, job search guides, Q&A](https://www.codefather.cn)
|
||||
|
||||
3) Programmer Interview Cheat Sheet: [Internship/campus/social recruitment high-frequency topics, company question analysis](https://www.mianshiya.com)
|
||||
|
||||
4) Programmer Resume Builder: [Professional templates, rich examples, direct to interviews](https://www.laoyujianli.com)
|
||||
|
||||
5) 1-on-1 Mock Interviews: [Essential for internship/campus/social recruitment interviews to land offers](https://ai.mianshiya.com)
|
||||
Reference in New Issue
Block a user