Build production-ready generative AI systems using LLMs, RAG, agents, and full-stack engineering practices
β±οΈ Length: 6.2 total hours
π₯ 59 students
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Course Overview
- This advanced installment of the Full Stack AI Engineer series propels learners beyond basic LLM interactions, directly into the intricate architecture and operationalization of next-generation generative AI solutions. It’s designed for engineers ready to define the future of intelligent applications, integrating cutting-edge LLMs into robust, scalable, and user-centric systems. The course emphasizes practical deployment and holistic lifecycle management, equipping graduates for the evolving 2026 AI landscape.
- Delve deep into the paradigm shift from traditional software development to AI-native engineering, exploring how generative AI redefines product design, user interaction, and backend infrastructure. Participants gain strategic understanding of leveraging LLMs as integral components of sophisticated systems capable of complex reasoning and dynamic content generation.
- Explore the critical nexus where innovative AI research meets practical, production-grade implementation. This module focuses on architectural choices, engineering tradeoffs, and deployment pipelines necessary to bring truly intelligent applications from concept to a fully operational state, impacting real users and businesses.
- Uncover methodologies for constructing resilient, adaptable AI systems that can evolve with new models and changing data. The curriculum emphasizes architectural patterns promoting modularity, testability, and maintainability, preparing engineers to build AI applications that stand the test of time and scale efficiently.
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Requirements / Prerequisites
- Intermediate Python Proficiency: Strong working knowledge of Python, including OOP, data structures, and experience with common libraries.
- Foundational ML/AI Concepts: Familiarity with basic machine learning principles, neural networks, and a high-level understanding of LLM operations.
- Basic Web Development Familiarity: Understanding of client-server architecture, HTTP, and some prior experience with a web framework (e.g., Flask, React) is advantageous.
- Command Line & Version Control: Competency in using the command line interface and practical experience with Git for collaborative development workflows.
- Analytical Mindset: A keen interest in breaking down complex technical challenges, debugging, and independently exploring solutions within this rapidly evolving domain.
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Skills Covered / Tools Used
- Advanced LLM API Orchestration: Mastering integration of diverse LLM providers, managing API rate limits, and orchestrating complex multi-model interactions.
- Vector Database Management: Hands-on expertise with leading vector databases (e.g., Pinecone, ChromaDB) for efficient similarity search and knowledge retrieval in RAG.
- Tool Creation & Function Calling for Agents: Developing custom tools and external function interfaces, empowering autonomous AI agents to interact with systems and augment reasoning.
- Asynchronous Backend Development (FastAPI): Building high-performance, asynchronous Python backends using FastAPI, focusing on robust API design, data validation, and real-time data streaming.
- Interactive Frontend Design for AI: Crafting intuitive, responsive user interfaces for generative AI applications, focusing on dynamic content display and optimal UX patterns.
- Persistent Memory & Session Management: Implementing strategies for stateful conversations, managing long-term memory for AI agents, and ensuring context continuity across user sessions.
- AI System Observability & Monitoring: Deploying tools for tracking LLM performance, latency, token usage, and output quality in real-time to maintain operational excellence.
- Data Governance & Compliance: Understanding and applying frameworks for responsible AI development, including data privacy, bias detection, and ethical considerations.
- Containerization & Deployment (Docker): Packaging and deploying full-stack AI applications using Docker, ensuring environment consistency and preparing for scalable orchestration.
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Benefits / Outcomes
- Become a Full Stack AI Architect: Transition to an architect capable of designing, building, and deploying end-to-end generative AI solutions, bridging AI research and practical application.
- Lead Innovative AI Projects: Gain confidence and technical acumen to lead critical projects involving advanced LLM integration, agentic systems, and custom AI application development.
- Master Production-Grade AI Deployment: Acquire crucial skills to move beyond prototypes, implementing robust, scalable, and secure AI systems meeting enterprise standards.
- Future-Proof Your Engineering Career: Position yourself at the forefront of AI innovation, mastering tools and methodologies defining software development for the next decade.
- Solve Complex Real-World Problems: Develop the ability to deconstruct intricate business challenges and reconstruct them as elegant, AI-powered solutions, enhancing productivity.
- Elevate Your Problem-Solving Toolkit: Integrate advanced AI techniquesβlike intelligent agents and sophisticated RAGβinto your arsenal, tackling scenarios intractable with traditional software.
- Contribute to Responsible AI: Implement best practices for AI safety, ethics, and governance, ensuring the AI systems you build are powerful, fair, transparent, and beneficial.
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PROS
- Highly Practical & Project-Oriented: Focuses heavily on hands-on building of actual systems, moving beyond theoretical knowledge to direct application.
- Industry-Relevant & Future-Focused: Addresses the most sought-after skills in the rapidly evolving Generative AI landscape, preparing learners for 2026 and beyond.
- Holistic Skill Development: Covers AI model interaction, backend, frontend, deployment, optimization, and ethical considerations, providing a well-rounded skillset.
- Direct Path to Production: Teaches methodologies for building production-ready systems, significantly enhancing employability for roles requiring deployment expertise.
- Addresses Critical AI Challenges: Directly tackles issues like hallucinations, scalability, and safety, equipping engineers to build robust and reliable AI.
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CONS
- Demanding Pace for Beginners: Given its advanced nature and broad scope, the course presents a steep learning curve for individuals without a solid foundation in programming and basic AI/ML concepts.
Learning Tracks: English,Development,Data Science
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