Build Cutting-Edge AI-Powered
Applications from Scratch
Generative AI Full
Stack Development
Master the art of full-stack development with Generative AI!🚀
5
44 enrolled students
Objective
The primary objective of Generative Full Stack Development is to build intelligent, end-to-end applications by integrating traditional full stack development skills (frontend + backend + database) with generative AI technologies such as large language models (LLMs), multimodal AI, and agent-based systems.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Course 1: Prompt Engineering
Learning Objectives:
- Understand the fundamentals of machine learning and generative AI.
- Differentiate between traditional ML and generative models.
- Learn ethical considerations and the power/responsibility balance in AI.
- Gain practical experience with prompt engineering and LLM APIs.
Topics:
- Introduction to ML & Generative AI – Learn the foundational concepts of machine learning and how generative AI differs.
○ How machines predict and output – Understand the underlying mechanism of prediction in ML.
○ What is Generative AI? – Explore how generative AI models produce new content.
○ Generative vs Traditional ML Models – Compare the goals and outputs of generative vs traditional models.
○ Applications of Generative AI – Discover real-world uses across industries.
○ Advantages and Disadvantages – Evaluate benefits and potential pitfalls.
○ Ethical Considerations – Discuss AI responsibility, bias, and fairness.
- Prompt Engineering – Learn how to craft and optimize inputs to guide LLM behavior.
○ Introduction to Prompt Engineering – Understand the role of prompt engineering in AI systems.
○ Prompt Engineering Patterns – Explore reusable prompt templates and patterns.
○ Advanced Patterns – Dive into complex prompt techniques for nuanced control.
○ Guidelines & Recommendations – Learn dos and don’ts for effective prompting.
○ Prompt Engineering with Commercial LLM APIs – Practice using APIs like OpenAI and Anthropic.
○ Prompt Engineering with Open-Source LLM APIs – Work with Hugging Face and local models.
○ Best Practices – Apply proven methods to ensure quality and reliability.
○ Prompt Tuning – Learn to fine-tune prompts for performance improvement.
- Projects and Exercises:
○ Hands-on exercises using Python – Practice prompts and API usage through Python scripts.
○ Mini Project: Craft a chatbot using different prompting techniques.
Course 2: Retrieval Augmented Generation (RAG)
Learning Objectives:
- Understand how to retrieve relevant information efficiently for LLMs.
- Learn the components of a RAG pipeline without pre-built frameworks.
Topics:
- High-Performance Vector Similarity Search – Discover how to match text with relevant information.
○ Introduction to Vector Search – Learn the principles behind similarity matching.
○ FAISS and its utility – Use Facebook’s FAISS library for scalable similarity search.
○ Encoding text into Embeddings – Convert text into numerical representations.
- Building Retrieval Systems – Build the full pipeline for fetching relevant context.
○ Loading Data – Learn to ingest structured and unstructured data.
○ Splitting and Chunking – Break down documents into manageable pieces.
○ Vector DBs and Retrievers – Store and search embeddings efficiently.
- Implementing RAG Systems – Combine retrieval and generation in one system.
○ System architecture – Understand the design layout of a RAG pipeline.
○ Evaluation techniques – Measure accuracy and relevance of responses.
○ Interaction with LLMs – Learn to feed retrieved content into LLMs.
- Project:
○ Build a Document Retriever Search Engine from scratch.
○ Mini Project: Implement a Q&A bot over a PDF knowledge base.
Course 3: Developing Applications with LLM
Learning Objectives:
- Gain in-depth knowledge of building LLM-powered apps.
- Master LangChain, LCEL, and function calling.
- Address safety, accuracy, and reliability of AI apps.
Topics:
- LangChain – Learn a powerful framework for chaining LLMs with tools and logic.
○ Inputs/Outputs – Understand how to pass and receive data from chains.
○ Prompt Templates – Use templates for dynamic prompt construction.
○ Chains, Callbacks, Memory – Manage state and interaction in multi-step flows.
○ OpenAI Functions, LCEL – Leverage structured inputs and custom logic.
- Application Building – Create real-world applications powered by LLMs.
○ Text apps: Recipe Generator, Study Buddy, History Bot – Build utility and educational apps.
○ Chat apps: Summarize, Classify, Generate names – Build interactive chat-based features.
○ Search apps: YouTube video search – Use LLMs for intelligent retrieval.
○ Image apps: Image generation – Integrate with generative image models.
○ Function Calling: Course finder app – Connect LLMs to APIs for dynamic tasks.
- Best Practices – Ensure quality, security, and robustness in LLM systems.
○ AI Security – Identify and mitigate security vulnerabilities.
○ Data Integrity – Maintain clean, reliable data.
○ Security Testing – Use adversarial testing and validation.
○ AI Red Teaming – Stress-test models for safety and ethics.
○ Customer Data Protection – Implement data privacy safeguards.
- Output Validation – Measure and improve model performance.
○ Accuracy & Hallucinations – Reduce false or misleading outputs.
○ Harmful content, Fairness – Detect bias and mitigate harm. ○ Risk mitigation – Reduce model failure and negative outcomes.
○ Content filtering, Guardrails – Implement constraints on model outputs.
○ Meta-Prompting, Grounding (RAG) – Use external context to stabilize outputs.
- Projects & Practice:
○ Practice: Evaluate and improve an LLM app.
○ Project: Build an AI-powered tutor app.
Course 4: Optimization and Deployment
Learning Objectives:
- Learn to optimize and deploy LLM apps efficiently.
- Understand trade offs between cost, speed, and accuracy.
Topics:
- Optimizing LLM Applications – Reduce cost and improve speed of AI apps.
○ Performance tuning – Adjust settings for speed and efficiency.
○ Batch vs Real-time apps – Choose the right approach for your use case.
○ Parallel execution – Run tasks concurrently to speed up pipelines.
○ LiteLLM – Use a lightweight interface for LLM calls.
○ Caching (token, local, SQL) – Reuse computations to save time and cost.
- Deployment
○ Render.com for deployment – Deploy AI apps using a modern cloud platform.
- Practice Examples:
○ Measure latency and cost tradeoffs using LiteLLM.
○ Deploy a chatbot on Render and document the steps.
Course 5: Agentic AI
Learning Objectives:
- Understand and implement agentic design patterns.
- Build autonomous agents and multi-agent systems.
- Apply LangGraph and agent protocols.
Topics:
- Introduction to Agentic Design – Learn how autonomous AI agents are structured.
○ What is Agentic AI? – Explore how agents independently decide and act.
○ Why use design patterns? – Understand the repeatable solutions in AI workflows.
- Design Patterns – Implement core agent capabilities.
○ Reflection Pattern: Theory & Build – Allow agents to assess and improve their output.
○ Tool Use Pattern: Theory & Build – Equip agents to use external tools and APIs.
○ Planning Pattern: Theory & Build – Guide agents in multi-step reasoning tasks.
○ Multi-Agent Pattern: Theory & Build – Enable collaboration between multiple agents.
- LangGraph – Build advanced agent workflows with graph-based control.
○ Components: Graphs, Chains, Nodes, States, Edges – Understand building blocks.
○ Features: Conditionals, Tools, Message filtering – Implement custom logic and flows.
○ Build: Research Agent – Create a knowledge-intensive autonomous researcher.
- Agentic Protocols – Learn industry standards for agent communication.
○ MCP (Open Source) – Explore open protocol for modular agents.
○ ACP (IBM) – Understand IBM’s secure agent framework.
○ A2A (Google) – Learn how Google’s agents collaborate.
- Projects & Practice examples:
○ Build a personal assistant agent using a reflection pattern.
○ Practice: Compare reflection vs tool-use agents in real-world tasks.
Frequently Asked Questions
1. What is Generative AI Full Stack Development?
Generative AI Full Stack Development combines traditional web and software development (frontend, backend, and databases) with generative AI technologies like large language models, image generation, and autonomous agents to build intelligent, AI-powered applications.
2. Who is this for?
This is ideal for developers, data scientists, AI enthusiasts, and tech entrepreneurs who want to build smart apps that use AI for content creation, chatbots, automation, image generation, or decision-making.
3. What are the key components of Generative Full Stack apps?
Frontend (React, Vue, etc.)
Backend (Node.js, Python/Django, etc.)
Generative AI APIs (OpenAI, Hugging Face, etc.)
Prompt engineering
Agent workflows
Database (MongoDB, PostgreSQL, etc.)
Deployment (Docker, Vercel, AWS)
4. Do I need prior AI or machine learning knowledge?
Basic programming knowledge is helpful. While prior AI/ML knowledge is not mandatory, understanding APIs and logic flow will help you grasp generative AI integration more effectively.
5. What can I build with Generative AI Full Stack Development?
AI chatbots and virtual assistants
AI-powered content creation tools
Smart dashboards with dynamic data summaries
Image or video generation apps
Educational tools powered by LLMs
Autonomous agent systems (multi-step task solvers)
Why Take This Course?
You will gain expertise in crafting effective prompts, leveraging Lang chain for AI workflows, and building autonomous AI agents. Develop hands-on skills to create intelligent, context-aware applications that enhance enhances automation and decision making.
What You’ll Learn:
✅ Generative AI Fundamentals
✅ Prompt Engineering
✅Retrieval Augmented Generation – RAG
✅High-Performance Vector Similarity Search
✅ Building Applications with LLM
✅ Optimizing LLM Applications
✅ Deploying AI applications
✅ Building Agentic AI
✅ Capstone Project on Agentic AI
Who Should Enroll?
Students
College students and current Software professionals interested in AI
Python Knowledge
If you’re familiar with Python programming
Software Developers
Software developers looking to upskill