What you'll learn
Design and apply effective prompt engineering techniques (structured, few-shot, multi-step) for real AI engineering tasks
Build end-to-end LLM applications using Python, HuggingFace Transformers, and modern Generative AI tools
Create Retrieval-Augmented Generation (RAG) systems using vector databases such as FAISS, Chroma, or Pinecone
Develop and deploy production-ready AI systems by combining prompt engineering, ML/DL models, and MLOps practices

Requirements
Basic Python programming knowledge (variables, loops, functions)
Basic understanding of programming concepts and logical thinking
A computer with internet access (Windows, macOS, or Linux)

Motivation to learn modern AI and Generative AI tools — no prior ML or LLM experience required

Description
Generative AI and Large Language Models (LLMs) are transforming how modern AI systems are built — and prompt engineering is now a core engineering skill, not just a trick.This course is designed for AI engineers, ML practitioners, and developers who want to build real-world AI systems using prompt engineering, Python, machine learning, deep learning, LLMs, RAG, and modern GenAI tools.Instead of treating prompt engineering as an isolated concept, you’ll learn how to integrate prompts into end-to-end AI workflows — from Python automation and data processing to LLM-powered applications, vector databases, and production-ready systems.What You’ll LearnIn this course, you will:Understand prompt engineering fundamentals and mindsetUse prompts to generate, debug, and document Python codeBuild ML and deep learning pipelines with prompt-assisted workflowsWork with Transformers, LLMs, and HuggingFace modelsDesign structured, few-shot, multi-step, and self-reflection promptsBuild Retrieval-Augmented Generation (RAG) systems using vector databasesUse FAISS, Chroma, and Pinecone for similarity searchApply prompt engineering to data cleaning, feature engineering, and evaluationFine-tune models using LoRA and parameter-efficient techniquesBuild and deploy production-ready AI applicationsApply MLOps practices with Git, Docker, and demo apps (Streamlit/Gradio)Create a professional AI portfolio with real projectsHands-On Projects You’ll BuildThis course is project-driven, not theory-heavy. You’ll build:Prompt-assisted Python automation scriptsData analysis & visualization workflows using promptsMachine learning & deep learning modelsNLP systems like sentiment analyzersComputer vision classifiers using CNNs and transfer learningLLM applications using HuggingFace TransformersA RAG-based AI assistant using vector databasesPrompt libraries for reusable AI workflowsEnd-to-end GenAI systems ready for deploymentThe final section focuses on capstone portfolio projects, such as:AI medical assistantAI resume analyzer & job matcherAI customer support agentMultimodal AI systems (text + images)Why This Course Is DifferentMost courses either:Teach prompt engineering in isolation, orTeach AI/ML without showing how LLMs and prompts fit into real systemsThis course bridges that gap.You’ll learn:When to use prompts vs codeHow prompts improve productivity for AI engineersHow to combine LLMs, ML models, vector databases, and automationHow modern AI systems are actually built in practiceWho This Course Is ForThis course is ideal for:Aspiring AI EngineersMachine Learning & Deep Learning practitionersPython developers moving into Generative AIData scientists working with LLMsSoftware engineers building AI-powered productsPrerequisitesBasic Python knowledge is helpful (a fast-track Python section is included)No prior experience with LLMs or prompt engineering is requiredBy the End of This CourseYou’ll be able to:Design effective prompts for real engineering tasksBuild LLM-powered AI systems end to endConfidently work with modern GenAI toolsShowcase multiple AI projects in your portfolioApply prompt engineering as a professional AI engineering skill