Applied AI with LLMs and Agentic Systems

Build Intelligent Assistants with LLMs and Agentic Systems

​A hands-on, project-based course to develop real-world AI applications using LLMs

🎯 “Ready to Take Your Applied AI with LLMs and Agentic Systems Knowledge to Next Level?”

Join our Live Coaching Program & Build Real-World Projects!

Limited to Only 10 Students per cohort

🔥 Early-Bird Offer – Limited to the First 5 Students!
The first 5 students to enroll will receive 40% off using the code AI40OFF at checkout! 🚀

Applied AI with LLMs and Agentic Systems

🕒 Course Duration: 9 Weeks | 2 Live Sessions (min) Per Week (2 Hours Each)
🔹 Total Live Hours: 36 Hours
🔹 Self-Paced Practice: Flexible Based on Your Availability
🔹 Live Q&A & Office Hours Every Weekend

What You’ll Gain from This Course

By the end of this course, you will:
✅ Build intelligent assistants and chatbots powered by Large Language Models (LLMs)
✅ Understand and apply core concepts like embeddings, memory, RAG pipelines, and agentic workflows
✅ Use APIs from OpenAI, Hugging Face, Cohere, and LangChain to build dynamic, interactive AI tools
✅ Structure and deploy your own custom LLM apps using frameworks like Streamlit, Gradio, and LangChain
✅ Implement semantic search, tool-calling, and memory chains for next-gen AI performance
✅ Create a GitHub-ready portfolio with 3–4 smart apps, including a full-featured capstone project
✅ Confidently apply LLMs to real-world tasks in content generation, summarization, document Q&A, and automation

🚀 This course is hands-on, fast-paced, and outcome-driven—perfect for developers, engineers, and professionals ready to build AI tools with real-world value.

Topics Covered:

✔ What is an LLM? Core concepts: tokenization, context windows, attention
✔ From RNNs and LSTMs to Transformers: the shift in NLP architecture
✔ Key LLM models: GPT, Claude, LLaMA, Mistral, BERT
✔ Open-source vs. proprietary models: Hugging Face vs. OpenAI vs. Anthropic
✔ Language-based AI use cases: text generation, Q&A, summarization, coding

What You’ll Learn:

✅ Understand the evolution of NLP from traditional models to transformer-based LLMs
✅ Gain clarity on how LLMs process, represent, and generate text
✅ Learn the capabilities and trade-offs of popular LLM architectures

Topics Covered:

✔ Prompt types: zero-shot, few-shot, chain-of-thought
✔ System, user, and assistant roles in structured prompts
✔ Parameters that shape LLM responses: temperature, top-p, stop sequences
✔ Practical skills: prompt debugging, rephrasing, and optimization
✔ Hands-on testing in OpenAI Playground and Python scripts
✔ Mini project: AI-powered content rewriter or tone/style transfer bot

What You’ll Learn:

✅ Develop effective prompts that yield consistent and controllable outputs
✅ Understand the principles of structured, reliable prompt engineering
✅ Build your first prompt-powered micro-app using OpenAI or Hugging Face APIs

Topics Covered:

✔ Calling OpenAI, Cohere, and Hugging Face APIs using Python
✔ Managing API keys, rate limits, and streaming responses
✔ Handling JSON output and extracting structured data
✔ Parsing and cleaning generated text for app use
✔ Project ideas: AI email assistant, resume enhancer, or content generator

What You’ll Learn:

✅ Learn to integrate LLM APIs into your own Python applications
✅ Understand cost management and token handling in real-time apps
✅ Build your first LLM-powered tool with real user input/output workflows

Topics Covered:

✔ What are embeddings? How vector space powers “AI memory”
✔ Text chunking and vectorization techniques
✔ Tools: FAISS, Pinecone, ChromaDB for similarity search
✔ Building search pipelines with vector databases
✔ Project: AI-powered semantic search across your personal document collection

What You’ll Learn:

✅ Apply embedding models to represent and search through unstructured data
✅ Build intelligent search experiences that go beyond keywords
✅ Understand how AI “remembers” context through vector similarity

Topics Covered:

✔ The architecture and concept of RAG pipelines
✔ Grounding LLM responses using private/custom data
✔ Combining embeddings + LLMs for contextual Q&A
✔ Tools: LangChain retrievers, LlamaIndex basics
✔ Project: Private PDF-trained chatbot that answers user queries

What You’ll Learn:

✅ Learn to build AI systems that “know your data” — grounded in truth
✅ Implement RAG workflows for accurate, document-aware AI apps
✅ Develop LLM chatbots that deliver more reliable, personalized answers

Topics Covered:

✔ Introduction to LangChain and its ecosystem
✔ Types of memory: buffer, summary, and vector memory
✔ Calling tools and APIs (e.g., calculator, search, weather) from within chatbots
✔ Structuring multi-step logic using LangChain’s chaining system
✔ Project: AI assistant with memory, context retention, and basic planning

What You’ll Learn:

✅ Add persistent memory and tool-using ability to your AI agents
✅ Create dynamic, multi-turn conversational systems
✅ Design logic chains to enable intelligent workflows with LLMs

Topics Covered:

✔ What is Agentic AI? Understanding agents, tools, and planning
✔ Overview of ReAct, AutoGPT, Toolformer, LangGraph
✔ Agent design patterns: guided vs autonomous execution
✔ Building agents that perform multi-step reasoning and decision-making
✔ Project: AI task agent that reads files, summarizes, and takes automated next steps (e.g., drafting emails or notes)

What You’ll Learn:

✅ Understand the architecture and flow of agentic systems
✅ Design LLM-powered agents that plan, reason, and act across multiple tools
✅ Explore the future of autonomous AI systems through real-world examples

Topics Covered:

✔ Creating user interfaces with Streamlit and Gradio
✔ Designing chatbot-style frontends for interaction
✔ Securing API keys, managing user inputs, and handling errors
✔ Deployment options: Hugging Face Spaces, Render, and Docker
✔ Best practices for sharing and hosting your AI tools

What You’ll Learn:

✅ Build web-based frontends to interact with your AI tools
✅ Deploy LLM apps for public demos or private use
✅ Learn the workflows to take your code from development to deployment

Topics Covered:

✔ Capstone guidance: choose your own AI assistant or agent project
✔ Walkthrough: scoping, designing, and building your final system
✔ UI integration + multi-tool logic
✔ GitHub portfolio preparation: README, walkthrough video, screenshots
✔ Peer feedback and final showcase

What You’ll Learn:

✅ Build and deploy your own full-featured LLM-based AI app
✅ Document your project in a GitHub-ready format
✅ Present your work with clarity and confidence to peers, recruiters, or clients

Why This Course?

🤖 Why Choose This Course?

💡 A hands-on, project-based learning journey designed to help you build and deploy intelligent assistants, chatbots, and AI-powered tools using the latest LLM technologies.

✅ Live, Interactive AI Learning (No Passive Watching)

Get real-time mentorship from PhD-level instructors with deep expertise in AI, NLP, and applied machine learning.
No pre-recorded lectures — every session is live, interactive, and designed for maximum engagement and clarity.

 

🚀 Build Real AI Apps – Not Just Prompts

You won’t just learn prompt tricks — you’ll design and deploy actual applications using APIs, LangChain, and agentic frameworks.
From document-aware chatbots to smart workflow agents, your projects will be practical, polished, and portfolio-ready.

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🧠 One-on-One Debugging & Support + Career-Focused Tools

Hit a roadblock? We help you debug prompts, code, APIs, and logic — step by step.
Live Q&As, office hours, and 1:1 support ensure you stay on track.
Learn tools used in startups and top AI teams: OpenAI, Hugging Face, FAISS, Streamlit, and more.

Frequently Asked Questions

This course is designed for:

  • Developers, engineers, data enthusiasts, and entrepreneurs who want to build intelligent assistants, AI agents, and LLM-powered tools

  • Anyone with basic Python and AI/ML understanding ready to apply it in real-world projects

  • Professionals in software, automation, education, or product development who want to integrate modern AI into their workflow

  • You’ll need:

    • Basic Python programming knowledge

    • Some familiarity with AI or machine learning concepts (e.g., what a model or token is)

    Don’t worry — we’ll guide you step by step through every tool and technique, with hands-on examples and real-world projects. If you’re motivated and curious, you’re good to go!

  • All live sessions are recorded & available for replay.
  • You’ll also have access to additional Q&A sessions to clarify any doubts.
  • We recommend 4-6 hours per week, including live sessions, project work, and Q&A time.
  • The more time you spend practicing, the faster you’ll master the concepts.

 

✅ Live, interactive coaching – Every session is instructor-led and project-focused.
✅ 1-on-1 support – Stuck on an API? Struggling with prompt formatting? We help you troubleshoot in real time.
✅ Practical focus – You’ll leave with deployed tools, not just theory.

This isn’t passive video watching — it’s an experience.

You’ll need:

  • A computer with Python and Jupyter installed (or Anaconda)

  • Internet access to work with APIs like OpenAI, Hugging Face, LangChain, etc.

  • A GitHub account to build your project portfolio

We’ll guide you through setting everything up — no GPU, cloud servers, or expensive infrastructure required. Everything is built to run on a regular laptop.

  • Yes! We offer a 100% refund within the first 7 days of your purchase.

Meet Your Instructor – Dr. Ata Ur-Rehman

PhD in Artificial Intelligence | Expert in Artificial Intelligence, Machine Learning, Computer Vision, and Web Development

Dr. Ata Ur-Rehman is an experienced computer scientist, researcher, and educator, specialising in Artificial Intelligence, Machine Learning, Computer Vision, and Web Development.  With a PhD from the Loughborough University, he has worked extensively on different applications of Machine Learning and Artificial Intelligence.  

🔹 Academic & Industry Experience
Dr. Ata ur-Rehman has taught at University of Sheffield, Ravensbourne University London, and other highly ranked universities in the world, delivering courses on Computer Vision, Artificial Intelligence, Machine Learning, Natural Language Processing, Data Analytics, Web3 and Block Chain, Cloud Computing, and Full Stack Web Development. His research contributions include anomaly detection, target tracking, medical imaging and data analytics.

🔹 Technical Expertise

  • Machine Learning, Deep Learning, NLP, LLMs, Generative AI, Transformers, and Agentic AI.
  • Languages (Python, R, JavaScript and SQL)
  • Libraries & Frameworks (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers)
  • Data Storage & Access (SQL, MongoDB, Apache Spark, AWS S3, and Azure Blob)
  • Cloud Platforms (Google Colab, AWS, Azure, and Databricks)
  • Dev Tools (Jupyter Notebooks, VSCode, GitHub, and Docker)
  • Deployment Frameworks (React, Flask, Django, and Express)

🔹 Passion for Teaching
With over a decade of academic experience, Dr. Ata Ur-Rehman has mentored computer Scientist and professionals, helping them design and build real-world AI projects from scratch. His courses blend theory with hands-on applications, ensuring learners gain practical, industry-relevant skills.