2. What skills will I develop through AI engineering training?

You can develop skills in LLM integration, prompt and context management, RAG development, embeddings, vector databases, AI agent development, API integration, and AI application architecture. The course also covers evaluation, security, deployment, monitoring, and system maintenance.

3. What is the difference between AI engineering and traditional software development?

AI engineering involves developing software systems that incorporate models such as LLMs, retrieval systems, AI agents, and other machine learning capabilities. It adds considerations such as model evaluation, prompt design, retrieval quality, AI reliability, and responsible system monitoring.

4. Does the course cover LLM, RAG, and AI agent development?

Yes. The curriculum combines all three areas within a single AI engineering pathway. Learners work with LLM APIs, build RAG pipelines, develop AI agents, connect external tools, and integrate these technologies into AI-powered applications.

5. Does the course cover AI application security and deployment?

Yes. Learners explore output testing, LLM performance evaluation, RAG monitoring, AI agent reliability, prompt injection prevention, data protection, infrastructure, deployment, and application monitoring. The final module also addresses maintaining and improving AI systems after deployment.

AI Engineering: Build LLM, RAG and AI Agent Applications

Category Data Analytics & AI

Level Experts

Students 18 Already Enrolled

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(4.7)

AI engineering focuses on building intelligent applications that combine large language models, retrieval systems, AI agents, APIs, and modern software workflows. This course provides a structured pathway for developing AI applications, from core architecture and LLM integration to RAG pipelines, agentic systems, evaluation, and deployment.

Learners explore LLM APIs, prompts, context, structured outputs, embeddings, vector databases, semantic search, hybrid retrieval, reranking, and RAG optimization. The course also covers AI agent architecture, memory, external tools, agentic workflows, application state, APIs, and integrated AI assistants.

Evaluation, security, and deployment are central to the program, including output testing, performance monitoring, prompt injection prevention, data protection, infrastructure, and production systems. Through an integrated project, learners bring LLM, RAG, and AI agent technologies together within a complete AI application.

AI engineering foundations and modern AI application architecture
LLM APIs, prompts, context, parameters, and structured outputs
RAG pipelines, embeddings, vector databases, and semantic search
Advanced retrieval, hybrid search, reranking, and RAG evaluation
AI agent architecture, memory, tools, and agentic workflows
Integrated AI applications, security, deployment, and monitoring

Basic knowledge of artificial intelligence, software development, or programming is recommended for this course. Familiarity with APIs, LLMs, or cloud-based applications can also help learners progress through the development-focused topics.

This course is designed for software developers, AI engineers, machine learning professionals, data professionals, technical product teams, and developers building generative AI applications. It is also suitable for technology professionals who want to develop integrated systems using LLMs, RAG, and AI agents.

1.1 Understanding AI Engineering
1.2 Modern AI Application Architecture
1.3 Large Language Model Fundamentals
1.4 RAG and AI Agent Concepts
1.5 AI Engineering Development Workflows
1.6 AI System Capabilities and Limitations

2.1 Working with LLM APIs
2.2 Managing Prompts and Context
2.3 Controlling Model Parameters
2.4 Generating Structured Outputs
2.5 Handling LLM Errors
2.6 Evaluating LLM Responses

3.1 Understanding RAG Architecture
3.2 Preparing Documents for Retrieval
3.3 Creating Text Embeddings
3.4 Working with Vector Databases
3.5 Implementing Semantic Search
3.6 Building RAG Pipelines

4.1 Improving Document Chunking
4.2 Implementing Hybrid Search
4.3 Reranking Retrieved Results
4.4 Using Metadata Filters
4.5 Evaluating Retrieval Quality
4.6 Reducing RAG Hallucinations

5.1 Understanding AI Agent Architecture
5.2 Designing Agent Instructions
5.3 Implementing Agent Memory
5.4 Connecting External Tools
5.5 Building Agentic Workflows
5.6 Managing Agent Decision-Making

6.1 Combining LLMs with RAG
6.2 Integrating RAG with AI Agents
6.3 Connecting External APIs
6.4 Building Multi-Step AI Workflows
6.5 Managing Application State
6.6 Developing AI-Powered Assistants

7.1 Testing AI Application Outputs
7.2 Evaluating LLM Performance
7.3 Monitoring RAG Retrieval
7.4 Testing AI Agent Reliability
7.5 Preventing Prompt Injection
7.6 Protecting AI Application Data

8.1 Designing Production-Ready AI Systems
8.2 Managing AI Application Infrastructure
8.3 Deploying LLM Applications
8.4 Monitoring AI Applications
8.5 Building an Integrated AI Project
8.6 Maintaining and Improving AI Systems

An AI engineering course is suitable for software developers, AI practitioners, ML professionals, data professionals, and technical teams developing intelligent applications. Learners with programming and software development experience can particularly benefit from the technical curriculum.

AI Engineering: Build LLM, RAG and AI Agent Applications

About AI Engineering: Build LLM, RAG and AI Agent Applications

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