必读 LLM 应用开发全栈指南 (English)

Generated: 2026-06-22 02:52:55

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You know what? Just half a year ago, I was absolutely fuming at a model that could only chat.

I said to it: "Can you help me organize this week's code commit history?"

It replied: "Sorry, I can't access external systems."

Isn't that infuriating? Just a glorified chatbot!

But guess what? In just these six months, I've watched it transform before my eyes. Now, it can write code, search for information, and automate tasks—a full-fledged "digital employee"!

Honestly, these past six months have been a mix of anxiety and excitement. Anxiety, because AI evolves at lightning speed—what I learned yesterday is useless today. Excitement? Because I've seen the future—a future I built with my own hands.

Alright, enough with the fluff. In this article, I'm going to spill all the pitfalls I've stumbled into and the experience I've accumulated over the past year. No highfalutin concepts—just practical, hands-on tips you can follow step by step.

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Let me first clear up a few terms you've definitely mixed up: LLM, Agent, Skills, MCP.

I'm not exaggerating—I was completely lost myself at the beginning.

LLM is the brain, not the hands and feet.

When I started my first project, I naively thought giving GPT an API would make it omnipotent. What happened? It could understand what you said, but when asked to query a database, send an email, or call an interface—it just said, "I can't do that."

Think about it—it's like asking a genius mathematician to move bricks: the brain works fine, but there are no hands!

Agent? A butler with hands and feet.

The first time I used LangChain to build an Agent, I had it help me organize my weekly report. It had to first understand the format I wanted, then pull code commit history from GitLab, query task status via Jira API, and finally generate a Markdown file. I didn't have to click a single thing manually!

When it first ran successfully, I was so excited I almost slammed the table—it worked! It really worked! That feeling was even better than the first time I got an API call right.

Skills, simply put, are plug-and-play capability modules.

I learned this the hard way. At first, I crammed all the logic into one Agent. Later, when I needed to change a feature, I had to redeploy the entire system—a huge hassle. Eventually, I wised up and broke down functions like "check weather," "send email," and "calculate data" into independent Skills—plug in whichever you need, like building with LEGO.

MCP is the master control console.

I only truly understood this recently. When you have just one Agent, manual management is fine. But imagine having five or six Agents, each handling different business lines—without a unified scheduling protocol like MCP, the system turns into chaos.

I tested it: without MCP, when multiple Agents accessed the same database simultaneously, data consistency collapsed! It was like an intersection without traffic lights—completely gridlocked.

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So, to sum up my experience in one sentence:

• Using only LLM → a chatbot, good for chatting
• LLM + Agent + Skills → enables personal automation, like auto-generating weekly reports or searching for information
• Adding MCP → makes enterprise-level applications possible, like automatically handling customer tickets or coordinating data across multiple departments

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The dumbest mistake I made on my learning journey was: obsessing over the underlying principles.

I spent two weeks poring over the Transformer paper, and what did I get? Not a single line of code written. Later, I realized it wasn't necessary at all!

First pitfall to avoid: Don't start with mathematical derivations.

I've put together a priority list for you to follow.

Must-learn, highest priority:

• Python basics: Don't dive into advanced topics like metaclasses or decorators—90% of it is useless. I only use Python 3.10 with three libraries: requests, json, and pandas. That's more than enough. Remember, Python here is just glue, not an algorithmic language.
• API calls: Register developer accounts for OpenAI, Tongyi Qianwen, and Claude. Learn to call APIs. From registration to writing your first "Hello World" conversation, it takes about half an hour if you're proficient.
• Prompt engineering: This has been hyped to the skies, but the core is just three things—clear instructions, scenario constraints, and format restrictions. My pitfall: Don't bother memorizing a bunch of "Prompt templates." They're useless. The key is to use System Prompt to define the role and Few-shot examples to guide the output.

Must-understand, medium priority:

• RAG (Retrieval-Augmented Generation): The core solution for document Q&A and knowledge base queries. My first RAG project was building an internal document Q&A system for my company. I used LangChain, and from document loading, splitting, vector storage to retrieval and generation, it took two days to get it running. The result was amazing—previously, finding a contract took half an hour; now, just ask and it appears.
• Agent concepts: Understand the ReAct pattern (reasoning + action), and learn to make the model call external tools. I used LangChain's Agent module to write a small tool that automatically checks the weather and calculates exchange rates—less than 50 lines of code.

Advanced learning, choose as needed:

• LangChain/LangGraph: The framework itself isn't hard; what's hard is understanding why it exists. I suggest manually calling APIs to write a few small projects first, then learn the framework. Otherwise, you'll get lost in abstract concepts.
• Fine-tuning: I only touch this when a project requires it. Honestly, 90% of scenarios don't need it.

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No matter how much theory you talk, it's better to write a single line of code.

I suggest you follow this pace—this is exactly how I trained myself.

Week 1: Python + API calls

• Spend 3 days learning Python basics, focusing on the requests library and JSON processing.
• Spend 4 days getting the large model API to work, writing a simple Q&A program.

Week 2: RAG in practice

• Use LangChain to build a knowledge base Q&A system. I used my company's internal documents as data, split them into 500-character chunks, and used Chroma for vector storage.
• Pitfall record: Too fine a split led to context loss; too coarse exceeded the model's context window. After a dozen tries, 500-800 characters worked best. Try it yourself and see.

Week 3: Agent development

• Start with a single Agent, teaching the model to use a calculator and check the weather.
• Then upgrade to multiple Agents—one handles data queries, another generates reports.
• Pitfall record: Setting too many Agent loop iterations leads to infinite loops. I set a limit of 3 iterations, and it worked well.

Week 4: Engineering

• Use FastAPI to build an API service and Docker for deployment.
• Add the MCP protocol to enable multiple Agents to work together.
• Pitfall record: On the first deployment, I forgot to add timeout handling, so the model got stuck and the entire service crashed. Later, I added a 30-second timeout and retry mechanism, and it stabilized.

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I've seen too many people collect a ton of resources but never even get a demo running. What's the problem? It's not a lack of intelligence—it's not taking action.

Today, I urge you to start with three things:

1. Register an API account and get your first conversation working.
2. Write a RAG program and have it answer a specific question for you.
3. Build an Agent and let it automate a repetitive task for you.

Once you've done these three steps, you've already surpassed 80% of the "theorists."

For advanced learning, I recommend a few practical resources: LangChain's official tutorials, DeepLearning.AI's LangChain course, and DataWhale's open-source tutorials. Don't just watch—follow along and write the code.

By 2026, the barrier to developing large model applications will get lower and lower, but the ones who truly make it happen will always be those who take action.

Don't let your knowledge gather dust in your bookmarks—let it shine in your hands. ✊