AI Engineering: Building AI Applications (LangChain, LLM APIs + more)

AI Engineering: Building AI Applications (LangChain, LLM APIs + more) | ZeroToMastery [Update 08/2025]
English | Size: 2.9 GB
Genre: eLearning

Learn to build AI applications using LLM APIs and cutting edge tools including LangChain, LangSmith, and LangGraph. This is developer training for the new era of programming.

What you’ll learn
Use OpenAI & Gemini APIs to build real-world AI applications
Craft effective prompts using proven engineering techniques
Build chatbots, voice apps, and image generation tools
Use LangChain to create agents that use memory and tools
Work with embeddings & RAG for smarter search and Q&A
Build multi-step agents with LangGraph and LangSmith
Analyze, debug, and improve LLM-powered apps
Build a full-featured AI Research Assistant from scratch

This course is your hands-on path to becoming a Generative AI engineer…someone who doesn’t just use AI, but builds with it.

You’ll start by leveling up your Python skills, mastering how to structure modular code, handle APIs, and manipulate data. Then, you’ll dive deep into the world of large language models (LLMs)—how they work, how they’re trained, and how to talk to them effectively through advanced prompt engineering.

From there, it’s all about application. You’ll learn how to build real-world AI-powered apps using the OpenAI and Gemini APIs—working with chat, image, and audio features. You’ll go even further by learning frameworks like LangChain for chaining prompts and building agents, and LangGraph for orchestrating stateful, multi-step workflows. You’ll give your apps memory using embeddings and vector databases, and learn to debug and scale production-ready systems with LangSmith.

And it’s not just theory. Throughout the course, you’ll build chatbots, intelligent image tools, search-driven Q&A systems, and more. The final capstone brings everything together as you build a research agent that uses retrieval, tools, and reasoning to generate high-quality summaries of real-world data.

This is how you go from experimenting with AI… to engineering it.

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