LLM Engineering for Multi-Agent Systems

🧠Course Description

LLM Engineering for Multi-Agent Systems is a hands-on certification course that equips professionals to build, evaluate, and scale intelligent applications powered by Large Language Models (LLMs). Covering key concepts like embeddings, attention mechanisms, vector databases, semantic search, prompt engineering, and fine-tuning, the course progresses into advanced topics including LangChain, retrieval-augmented generation (RAG), and multi-agent architectures.

Through real-world case studies and a capstone project, learners will gain practical experience developing collaborative LLM agents and scalable pipelines. By the end of the course, participants will be ready to engineer enterprise-grade LLM solutions that are modular, adaptive, and production-ready.

🎯Objectives

By the end of this course, participants will be able to:

1. Master the LLM Ecosystem: Understand the architecture, evolution, and foundational components of Large Language Models, including embeddings, attention mechanisms, and transformers.
2. Evaluate Adoption Challenges: Identify key risks, deployment challenges, and practical considerations in adopting LLM-based systems at scale.
3. Engineer Intelligent LLM Workflows: Design and implement advanced prompt engineering, semantic search, and fine-tuning strategies to customize and enhance LLM performance.
4. Build with LangChain and RAG: Construct modular pipelines using LangChain and integrate retrieval-augmented generation (RAG) techniques to boost retrieval and reasoning capabilities.
5. Develop Multi-Agent LLM Systems: Architect and deploy collaborative multi-agent LLM applications capable of solving complex, coordinated tasks.
6. Evaluate and Deploy Solutions: Assess system performance using evaluation metrics and best practices, culminating in a hands-on project that delivers a production-ready multi-agent solution.

👥Target Audience

This course is ideal for:

• Machine learning engineers, data scientists, and AI practitioners specializing in LLMs and multi-agent systems.
• Software engineers and developers building intelligent, scalable LLM-powered applications.
• AI solution architects and technical product managers involved in designing and deploying enterprise AI systems.
• Researchers and advanced students seeking practical experience with cutting-edge LLM engineering.
• Professionals in NLP, conversational AI, and intelligent automation who are aiming to deepen their skills.

A foundational knowledge in machine learning, natural language processing, and software development will help maximize the learning experience.