Here is the English version of the modern AI course syllabus, designed to focus on cutting-edge skills like Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI.

Course Syllabus: AI Engineer — From Prompting to Autonomous Agents

• Duration: 6–8 months (~150 hours of practical work).
• Who it is for: Developers, data analysts, product managers, and professionals looking to integrate AI into real-world business workflows. (Features two tracks: No-Code for general automation and Code for developers).
• Tech Stack: Python, PyTorch, OpenAI API, Anthropic API, LangChain / LangGraph, CrewAI, Pinecone, Hugging Face, Docker.

🗺️ Curriculum Roadmap

Module 1: AI Foundations & Advanced Prompt Engineering (Weeks 1–4)

Learn how Transformer models work under the hood without getting lost in math. Master advanced prompting techniques to get predictable, high-quality outputs from leading LLMs (ChatGPT, Claude, Gemini, DeepSeek).

• Key topics: Chain-of-Thought, Few-Shot prompting, System Instructions, and mitigating model hallucinations.

Module 2: Python & LLM APIs (Weeks 5–10)

Transition from manual prompting to automation. Learn Python basics tailored for data handling and connect directly to LLM APIs.

• Key topics: Python for AI, data manipulation with Pandas, connecting to OpenAI and Anthropic APIs, and structuring unstructured text data.

Module 3: RAG (Retrieval-Augmented Generation) Architecture (Weeks 11–16)

Connect AI to your private or company-specific documents. Learn how to build a search system that feeds relevant data to the LLM to get highly accurate, context-aware answers.

• Key topics: Text chunking, vector embeddings, vector databases (Pinecone, ChromaDB), and hybrid search strategies.

Module 4: Agentic AI & Multi-Agent Systems (Weeks 17–22)

Build autonomous AI systems that don't just chat, but actually act. Design agents that can plan tasks, use external tools (web search, calculators, database queries), and self-correct their errors.

• Key topics: Agentic workflows, tool calling, and multi-agent frameworks like CrewAI, LangGraph, and AutoGen.

Module 5: Fine-Tuning & Open-Source Models (Weeks 23–26)

Learn how to run, optimize, and customize open-source models locally or on budget-friendly cloud instances.

• Key topics: Fine-tuning open models (like Llama), Quantization, and deployment using Docker.

🛠️ Portfolio Projects

You will build three major hands-on projects:

1. Enterprise RAG Assistant: An intelligent QA system that answers complex questions using only a specific set of internal company PDFs, spreadsheets, and databases.
2. Multi-Agent Automation Workflow: A team of autonomous AI agents working together (e.g., Researcher Agent gathers online data, Writer Agent drafts an article, and Editor Agent refines and posts it).
3. Generative Media Engine: A unified application that generates marketing copy, social media images (via Midjourney/Flux API), and video scripts from a single text prompt.

🎯 Career Outcomes

• No-Code Track Graduates: Step into roles like AI Integrator or AI Specialist. You will know how to plug ready-made AI models into standard business tools (via Zapier or Make) to automate operations.
• Code Track Graduates: Step into roles like AI Engineer. You will have the skills to design, code, and deploy custom intelligent agents and RAG pipelines directly into production codebases.