Free AI Application Developer Course Online with AI Tutor
This free AI developer course offers about 28 hours of curated video lessons and AI-tutor quizzes. You'll complete 107 lessons, starting with the Foundations of AI, ML, and Deep Learning. By the end, you'll know how to build advanced AI agents, manage prompt contexts, and deploy enterprise-grade AI applications.
What you'll learn in this AI Application Developer course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Design and deploy AI applications using foundational models and advanced agent architectures.
Master prompt engineering and context management for effective AI model customization.
Implement retrieval-augmented generation (RAG) techniques for enhanced data processing and application development.
Develop and optimize AI agents and workflows for real-world applications and enterprise-scale solutions.
Ensure robust AI operations with deployment, monitoring, and security strategies.
AI Application Developer course syllabus
01 Module 1: Foundations of AI Application Development
- Foundations of AI, ML, and Deep Learning
- Generative AI Models, Applications, and Tools
- From Prototype to Production: UX and Model Consistency
- Scaling AI Applications: Evaluations, Orchestration, and LLM Ops
- The Agent Development Lifecycle and Building with Deep Agents
- Deploying Agents and Introducing SmithDB for Observability
- SmithDB Architecture and the Future with Langsmith Engine
- The Agentic Workflow: From Concepts to Practical Skills
- Emerging Tools and Future Trends in AI Development
- The Journey to GPT-5 and New Foundational Models
- The Future of Coding with Codex
- Building Intelligent Agents with Agent Kit
- Creating Interactive Apps in ChatGPT with the Apps SDK
02 Module 2: Core APIs, Prompting, and Context Management
- Mastering the Art of Prompt Engineering
- The Evolution and Fundamentals of Structured Outputs
- Advanced Applications: Data Extraction, UI Generation, and Agentic Workflows
- Under the Hood: Engineering, Research, and API Design
- Introduction to GPT-5 and the Responses API
- Live Demo: Building a Minecraft Clone with CodeX
- Advanced Prompting Techniques for GPT-5
- Real-World Application: An Autonomous Coding Agent & Q&A
- Customizing AI Models for Domain-Specific Applications
- The Four Strategies of Context Engineering
- Implementing Context Engineering with LangGraph
- Foundations of Agent Memory and Context Engineering
- Short-Term Memory Techniques: Trimming, Compaction, and Summarization
- Demo: Implementing Trimming, Summarization, and Cross-Session Memory
- Advanced Memory Patterns, Best Practices, and Q&A
03 Module 3: Building with Retrieval-Augmented Generation (RAG)
- Introduction to Retrieval-Augmented Generation (RAG)
- Fundamentals of Vector Search and Embeddings
- Building and Applying Vector Search with RAG
- Building Data Pipelines for AI Applications
- Foundational Concepts: Embeddings and the Databricks Platform
- The RAG Workflow and Fivetran Data Ingestion Demo
- Data Transformation and Vectorization with Pinecone
- Executing the RAG Application and Final Q&A
- Getting Started with Vector Databases Using Pinecone
- Building a Self-Reflective RAG Agent with LangGraph
- Case Study: Architecting RAG for Enterprise Scale
- Introduction to RAG and Vertex AI Search Components
- Live Demo and Customer Case Study: Discover Financial
- Case Study: Palo Alto Networks & Enterprise-Ready RAG
04 Module 4: Developing AI Agents and Workflows
- Introduction to LangGraph and Basic Workflow Patterns
- Advanced Workflows and Building Agentic Systems
- Implementing a Basic Agent Executor Loop
- Understanding Built-in Tools: Concepts and Capabilities
- Experimenting with Tools in the OpenAI Playground
- From Playground to Production: Building a Data Dashboard
- Real-World Application: Heavia's AI Agents & Q&A
- Introduction to Advanced Chatbots and Environment Setup
- Building a Tool-Using Agent with User Confirmation
- Advanced Agent Design with Specialized Workflows
05 Module 5: Advanced Agent Architectures
- A Conceptual Guide to Multi-Agent Architectures
- Introduction to LangGraph and Multi-Agent Collaboration
- Agent Supervisor and Hierarchical Team Architectures
- Building Hierarchical Agent Teams with a Supervisor
- Understanding and Implementing Agent Memory
- Introduction to Agent Reflection and the Reflexion Architecture
- Advanced Reflection with Language Agent Tree Search (LATS)
- From Agentic AI to a 'Teammate' Architecture
- Implementation Strategies and Lessons Learned
06 Module 6: Multimodal and Specialized Applications
- How Multimodal AI Models Work
- Introduction to Real-time Voice Models and Live Demos
- Productionizing Voice AI: A Case Study with Sierra
- Real-time Voice Q&A and Implementation Best Practices
- Understanding Image Generation: Capabilities and Best Practices
- Live Demo: Building an AI Photo Booth Application
- Customer Spotlight with Gamma and Live Q&A
- Developing a Text-to-SQL Agent for Data Visualization
- Enabling Agents to Interact with Desktop Applications
- Building Customer Support Chatbots with No-Code Tools
- Creating Multi-Channel AI Agents for WhatsApp and Web
07 Module 7: AI Operations: Deployment, Monitoring, and Security
- Optimizing for Model Accuracy
- Optimizing for Latency and Cost
- A Framework for Evaluating AI Agents in Production
- Advanced Evaluation Strategies for Production AI Systems
- Getting Evaluations Right for LLM Applications
- How Lyft Builds Production-Ready AI Evaluations
- Building Evaluation Systems That Scale in Production
- Debugging and Observability with Tracing
- Deploying AI Applications with FastAPI and Docker
- The Agent Development Lifecycle and Building with Deep Agents
- Productionizing Agents: LangSmith Tooling and SmithDB
- Automating the Flywheel with Langsmith Engine
- Introduction to Multi-Agent Systems and Project Setup
- Configuring Agent Core Gateway and Initial Agent Deployment
- Enhancing the Agent with Memory and App Integration
- Testing, Observability, and Analysis
- Introduction to Google's Agent Development Kit (ADK) and Agent Engine
- Setting Up Your Google Cloud and Local CLI Environment
- Deploying, Testing, and Monitoring Your Agent with Agent Engine
- Foundations of Prompt Caching: Mechanics and Benefits
- Applying Caching: Demos and the Prompt Cache Key
- Advanced Optimization: Context Engineering and API Strategies
- Real-World Implementation: Warp Customer Spotlight and Q&A
- Fundamentals of AI Application Security
- Securing AI Agents with Auth0 Identity Framework
- Understanding OAuth Security for AI Applications
08 Module 8: The Modern AI Developer Experience
- Introduction to Codex: Installation and Configuration
- Core Usage: Prompting Patterns and CLI/IDE Tips
- Advanced Integrations and Programmatic Workflows
- The AI App Development Stack: Front-End and Back-End Tools
- Live Demo and Workflow: Building with Cursor and Future Outlook
- Case Study: How Uber Automates with LangGraph Agents
- Case Study: JP Morgan's AI Agent for Investment Research
- Amplifying Expertise with a Go-to-Market AI Assistant
- Scaling HR and Support with AI Agents & Key Takeaways
Real-world projects you'll work on
Practice the exact kind of work AI Application Developer professionals do on the job.
Building a Scalable AI-Powered Customer Support System for Amazon
Developing a Real-Time Voice-Activated Assistant for Tesla's Smart Vehicles
Architecting a Multi-Agent Investment Research Platform for JP Morgan
Creating a Multimodal AI-Driven Marketing Campaign for Coca-Cola
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this AI Application Developer course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Software engineers looking to transition into AI application development.
- Computer science students wanting practical experience with RAG and AI agents.
- Tech professionals aiming to automate workflows using large language models.
- Data enthusiasts who want to build real-world AI apps instead of just analyzing data.
Prerequisites
- Basic understanding of programming (preferably Python) is recommended.
- Familiarity with general software development concepts.
- No prior machine learning experience is strictly required, but it helps.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete AI Application Developer course: free, structured and job-focused
This artificial intelligence developer course packs about 28 hours of learning into 107 video lessons, paired with interactive AI-tutor quizzes. You'll start with the Foundations of AI Application Development, moving quickly into Core APIs, Prompting, and Context Management. As you progress, the curriculum covers Building with Retrieval-Augmented Generation (RAG) and Developing AI Agents and Workflows. You'll also explore Advanced Agent Architectures, Multimodal and Specialized Applications, and the Modern AI Developer Experience.
LearnTube makes learning practical by grouping curated YouTube videos into structured modules, followed by immediate AI-generated quizzes to test your knowledge. You won't just watch videos; you'll build four major hands-on projects. These include a scalable AI-powered customer support system for Amazon, a real-time voice-activated assistant for Tesla's smart vehicles, a multi-agent investment research platform for JP Morgan, and a multimodal AI-driven marketing campaign for Coca-Cola.
The platform is entirely free to learn, with an optional paid certificate available at the end. Before finishing, you'll master AI Operations: Deployment, Monitoring, and Security. This ensures you know how to safely launch the AI workflows you create. If your goal is to master prompt engineering or build multi-agent systems, this AI app development course gives you the exact skills needed to succeed as a full stack AI developer.
Jobs you can target after learning AI Application Developer
AI Application Developer is a gateway skill for some of the fastest-growing roles in tech.
Builds and deploys software applications powered by large language models and machine learning.
Designs and refines prompts to optimize the output and behavior of AI models.
Handles both the front-end interface and back-end AI integrations for intelligent applications.
Typical entry-level salary ranges in India as of September 2026. Actual compensation varies by city, company and experience.
Earn a AI Application Developer certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this AI Application Developer course.
Is this AI application development course worth taking?
Yes. Companies are actively hiring developers who can build practical AI tools. You'll learn highly demanded skills like RAG implementation and multi-agent architecture, making you a strong candidate for modern software engineering roles.
Are there any AI developer course fees?
Learning on LearnTube is completely free. You can watch all 107 video lessons and take every AI-curated quiz without paying. A verified certificate of completion is available as an optional paid add-on.
How does this differ from standard YouTube tutorials?
Instead of searching for random videos, you get a structured 8-module curriculum. We pair curated YouTube lessons with an AI tutor that tests your knowledge immediately, ensuring you actually understand concepts like context management.
Will I get a certified artificial intelligence developer document?
You can purchase an optional certificate after finishing all modules and quizzes. This document proves you completed the 28-hour curriculum and built four real-world AI projects, which you can easily add to your resume.
Is this a good AI developer course for beginners?
It works best if you already know basic programming. The curriculum starts with foundational concepts in machine learning before moving into advanced topics, so you don't need prior AI experience to succeed here.
How long does this AI app development course take?
The entire curriculum takes about 28 hours to complete. Since it is self-paced, you can study on your own schedule, finishing the 107 lessons in a few days or spreading them out over weeks.
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