一位早期职业 AI 工程师在 DEV 上记录用动手构建学习 AI 的历程
Hello DEV 👋 I’m Learning AI by Building
一位早期职业软件/AI 工程师在 DEV 发布首帖，宣布以“边构建边学习”的方式记录 AI 工程学习历程。他近期用 Python、FastAPI、LangChain、Hugging Face、向量数据库、Azure、AWS 和本地 LLM 做实验，并搭建了一个 RAG 系统，由此关注文档分块、嵌入模型选择、相关性检索、上下文过滤、答案接地评估和部署等问题。
After spending a lot of time learning about machine learning, NLP, LLMs, and cloud technologies, I’ve realized something:
The fastest way for me to learn is to build.
So, I’m starting to document that journey here on DEV.
I’m an early-career software/AI engineer interested in AI engineering, LLM applications, RAG systems, cloud, and backend development. Over the past few months, I’ve been experimenting with technologies such as Python, FastAPI, LangChain, Hugging Face, vector databases, Azure, AWS, and local LLMs.
One of the projects I recently worked on was a Retrieval-Augmented Generation (RAG) system.
Instead of simply asking an LLM a question and hoping it knows the answer, the system retrieves relevant information from a knowledge base and uses that context to generate a response.
At a high level:
Text
Documents
↓
Document Processing
↓
Chunking
↓
Embeddings
↓
Vector Store
↓
Similarity Search
↓
Relevant Context
↓
LLM
↓
Answer
Building it made me realize that RAG isn't just about connecting a vector database to an LLM.
There are a lot of interesting engineering decisions involved:
- How should documents be chunked?
- Which embedding model should be used?
- How do you retrieve the most relevant information?
- How do you prevent irrelevant context from reaching the LLM?
- How do you evaluate whether the generated answer is actually grounded?
- How do you handle documents that change over time?
- How do you turn a prototype into something that can actually be deployed?
These are the kinds of questions I want to explore more deeply.
What I'm learning next
I want to go beyond simple chatbot projects and explore:
- Agentic RAG
- Multimodal AI
- MCP and tool-using agents
- LLM evaluation
- AI application observability
- FastAPI and production backend architecture
- Azure/AWS AI services
- Local and open-source LLMs
This is my first post here, so I'm excited to start documenting the journey.
If you're also learning AI engineering, backend development, or LLM systems, I'd love to learn from what you're building too.
来源：Google AI：DEV 作者专属（RSS） · dev.to