中文

AI App Development Path

From your first API call to a deployed RAG application

Basic Python required ~2–3 months (1–2 hours/day) 8 steps

Path goal

Finish able to call mainstream LLM APIs, write solid prompts, use frameworks like LangChain/LlamaIndex or low-code platforms like Dify, build a knowledge-base RAG app, ship a shareable demo UI and deploy it.

Who it's for：Developers with basic Python who want to turn AI capabilities into real products

  1. Step 1: Solidify Python and API basics

    Make sure you're comfortable with requests, dict/list comprehensions and core syntax, and understand HTTP and JSON.

    💡 Skip this if you already know Python — go straight to API calls.

  2. Step 2: Get your first LLM call running

    Register on a domestic platform (no special network needed) or an overseas one, make your first chat call in 20 lines, and understand core parameters like temperature and system prompts.

    💡 DeepSeek, Tongyi, Zhipu and Kimi all have free quotas for new users; Google AI Studio and OpenRouter let you try many models after signup.

  3. Step 3: Learn prompt engineering systematically

    Master role setting, few-shot examples and chain-of-thought; learn to reliably get structured JSON out of prompts.

    💡 The Prompt Engineering Guide (Chinese) is the main textbook; the Instructor library stabilizes structured output.

  4. Step 4: Master mainstream frameworks

    Study LangChain's chains and LlamaIndex's data framework, and learn when to use a framework versus plain HTTP.

    💡 Frameworks move fast — grasp the concepts and look up API details as needed.

  5. Step 5: Build a RAG knowledge-base app

    Learn document parsing, embedding, retrieval and reranking; build a Q&A assistant over your own docs and measure quality with evaluation metrics.

    💡 Use RAGFlow/FastGPT for quick wins; read the LangChain RAG tutorial and Pinecone's learning center for depth.

  6. Step 6: Validate fast with low-code

    Build an AI app visually with Dify or Coze to validate the product idea before committing to engineering.

    💡 Low-code fits MVP validation; complex logic still needs code.

  7. Step 7: Local deployment and privacy

    Run open-source models locally with Ollama or LM Studio and learn vLLM serving basics for data-sensitive scenarios.

    💡 A 7B-class model runs on a 16GB machine.

  8. Step 8: Ship a shareable UI

    Wrap the model into a web app with Gradio or Streamlit, add multimodal ability, and send it to friends for real use.

    💡 Let real users in early and iterate — better than polishing behind closed doors.

Browse all resources →