Create Smart Applications with Large Language Models

Are you ready to step into the future of AI development? This comprehensive course will teach you how to build real-world applications powered by Large Language Models (LLMs) like OpenAI’s GPT, Anthropic’s Claude, Meta’s LLaMA, and Mistral. Whether you’re a developer, engineer, student, or tech enthusiast, this course will guide you through everything from foundational theory to advanced implementation using modern tools like LangChain, LlamaIndex, and vector databases.

In this hands-on, project-based course, you’ll understand how LLMs work and how they differ from traditional models. Learn the fundamentals of prompt engineering that guide model behavior effectively, explore embedding techniques, and how to index knowledge for scalable applications. You’ll also build your own intelligent agents, chatbots, and assistants while utilizing semantic search, cosine similarity, and vector stores for real-time knowledge retrieval.

With tools like Python, LangChain, OpenAI API, and Hugging Face, you’ll create an LLM-powered conversational travel assistant from scratch. We also include a Python crash course to ensure every learner has the coding foundation to follow along. By the end of this course, you’ll not only understand how to integrate LLMs into your applications, but you will have the practical skills to build your own intelligent tools and deploy them into the real world. No prior AI experience is required—let’s build the future together!

What you will learn:

• Build intelligent applications using large language models (LLMs) like GPT and Mistral.
• Design effective prompts to guide LLM behavior using advanced prompt engineering techniques.
• Compare and evaluate popular LLMs for different application scenarios.
• Implement retrieval-augmented generation (RAG) with embeddings and vector databases.
• Use LangChain to create dynamic, modular AI-powered workflows.
• Create conversational agents and assistants capable of natural, context-aware dialogue.
• Embed custom data into LLM pipelines using semantic chunking and indexing.
• Apply few-shot learning strategies to improve response quality in LLM outputs.
• Integrate external tools and APIs with LLM agents for enhanced functionality.
• Deploy Python-based AI applications with real-world usability and scalability.

Course Content:

• Sections: 7
• Lectures: 50
• Duration: 7h 5m

Requirements:

• No requirements, you’ll learn everything here! Including Python.

Who is it for?

• Software developers and engineers who want to integrate large language models into real-world applications and tools.
• Data scientists and AI enthusiasts looking to build intelligent agents using LangChain, embeddings, and prompt engineering.
• Entrepreneurs and product builders aiming to create AI-powered products like chatbots, assistants, or RAG systems with minimal overhead.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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