🌍 Course Overview

Modern organizations want more than experiments, they want AI applications that solve real business problems, integrate with workflows, reduce workload, and improve decision-making.

This course teaches you how to design and build business ready LLM applications using LangChain, LangGraph, vector search, embeddings, and Streamlit but more importantly, it teaches you how to:

·       Frame business use cases

·       Translate vague ideas into technical requirements

·       Design RAG workflows that produce reliable, auditable responses

·       Build prototypes that stakeholders understand

·       Align AI solutions with data, compliance, and governance needs

·       Deliver tools that reduce manual effort, improve quality, and create measurable value

By the end of the course, you won’t just know how to use LangChain — you’ll know how to think, design, and deliver like an AI Solutions Architect.

🎯 Course Objectives

Business & Strategy

By the end of this course, learners will be able to:

·       Identify high value AI opportunities that can realistically be automated with LLMs

·       Translate business pain points into technical workflows (RAG, agents, memory, summarization, automation)

·       Understand where LLMs add value and where they do not

·       Work with product managers, SMEs, and engineers to define requirements

·       Maintain alignment with compliance, data governance, and responsible AI principles

Technical & Implementation

Learners will also be able to:

·       Design LLM applications using LangChain and LangGraph

·       Use embeddings and vector search (FAISS/Chroma) to turn unstructured content into searchable knowledge systems

·       Build retrieval augmented generation (RAG) workflows that reduce hallucinations

·       Apply prompting, memory, summarization, and routing strategies

·       Build user-friendly UIs using Streamlit or Gradio

·       Convert a business requirement into an end-to-end, functional application

💡 Why This Course Is Different

Most “LLM courses” teach tools.
This course teaches how to deliver business impact.

What makes it unique:

✔ Business-first approach

You learn how organizations (federal, enterprise, consulting) identify opportunities, evaluate LLM feasibility, and turn requirements into real products.

✔ Designed by a practitioner actively building AI systems

You learn from someone who builds custom RAG, copilots, and workflow automation tools for real organizations.

✔ Hands-on, end-to-end architecture

You don’t just write prompts you design entire applications that work in real business workflows.

✔ Focus on reliability, trust, and accuracy

You learn retrieval, memory, and evaluation techniques that dramatically reduce hallucination and improve consistency.

✔ You graduate with a functional RAG application

You build a complete, portfolio-ready AI app (ChatToPDF) that demonstrates real capability.

🛡 How This Course Keeps You Relevant in the AI Era

Many learners worry: “Will AI replace coding?”

The reality: AI speeds up coding but it cannot replace people who understand how systems work.

What AI can do:

·       Write small code snippets

·       Suggest improvements

·       Help debug

·       Generate documentation

·       Produce quick prototypes

What AI cannot do:

·       Architect full applications

·       Understand business rules or context

·       Choose the right data or retrieval workflow

·       Ensure accuracy, safety, and compliance

·       Deliver production-ready tools

·       Make judgment calls

What YOU will learn:

·       How to define business problems clearly

·       How to design LLM + RAG pipelines that solve them

·       How to evaluate and improve AI outputs responsibly

·       How to build reliable, end-to-end applications

·       How to work with AI as a coding accelerator

This is the modern skillset companies are hiring for — humans who can architect solutions, validate outputs, and integrate AI into real workflows.

👥 Who This Course Is For

Perfect for:

·       Developers & Python users wanting to move into LLM app development

·       Data scientists wanting to create AI apps, not just notebooks

·       Analysts & consultants designing AI-driven workflows for clients

·       Product managers exploring AI use-case definition + prototyping

·       Technical beginners who want to understand how LLM systems work in real organizations

·       Anyone wanting to build searchable, context-aware, domain-specific AI assistants

🚀 Your 7-Week Journey

Week 1: From Business Need to First Chatbot

Business Value:
Learn how organizations identify AI opportunities and convert vague ideas (“We want a chatbot”) into actionable components.

Technical Focus:
Environment setup, API keys, VSCode, LangChain basics, building your first chatbot.

Mini Project:
A functional TopicBot with real API calls.

Week 2: Prompt Engineering With a Business Lens

Business Value:
Prompts determine accuracy, consistency, brand voice, and compliance.

Technical Focus:
PromptTemplate, ChatPromptTemplate, zero-shot vs few-shot, model comparison.

Mini Project:
A Prompt Playground for testing prompt quality — similar to enterprise evaluation workflows.

Week 3: Memory, Context, and Relevance with LangGraph

Business Value:
Why enterprise systems need memory: case notes, customer history, multi-step tasks.

Technical Focus:
LangGraph checkpointers, memory workflows, thread persistence.

Mini Project:
A Personal Assistant that remembers user preferences.

Week 4: Summarization & Document Intelligence

Business Value:
Long-document summarization powers compliance reviews, reporting, meeting intelligence, and research workflows.

Technical Focus:
Map-reduce vs refine summarization, chunking, extracting actionable insights.

Mini Project:
A Meeting Notes Summarizer.

Week 5: Semantic Search & RAG for Real Business Use

Business Value:
RAG is the foundation of enterprise knowledge assistants: policies, contracts, SOPs, HR documents.

Technical Focus:
Embeddings (OpenAI/HF), FAISS/Chroma vector stores, semantic retrieval pipelines.

Mini Project:
A FAQ Assistant that delivers citation-friendly answers.

Week 6: Capstone: Full ChatToPDF RAG Application

Business Value:
This is the same architecture used in real tools like policy copilots, contract copilots, and research assistants.

Technical Focus:
PDF parsing → embeddings → vector DB → retrieval → generation → Streamlit UI.

Mini Project:
Build your own ChatToPDF Web App.

Week 7: Presenting, Evaluating, and Scaling Your AI App

Business Value:
Learn the “last mile”: evaluation, governance, monitoring, cost control, and deployment.

Technical Focus:
Demo, documentation, Hugging Face Spaces, Streamlit Cloud deployment.

Mini Project:
Final architecture walkthrough + app demo.

🛠 What You Will Build

·       AI Chatbot with prompt templates

·       Memory-based assistant

·       Summarization engine

·       FAQ semantic-search bot

·       Full ChatToPDF RAG application

·       Architecture diagrams + documentation

·       Reliability & evaluation checklist

📈 Skills You’ll Gain 

Strategic & Business Skills

·       How to define valuable AI opportunities

·       How to turn ambiguous problems into technical designs

·       How RAG and LLM systems support knowledge-heavy workflows

·       How to evaluate feasibility, cost, accuracy

·       How to communicate architecture & value to non-technical teams

Technical Skills

·       LangChain & LangGraph fundamentals

·       Prompt engineering for structured business outputs

·       Memory systems & multi-turn flows

·       Summarization pipelines for long documents

·       Embeddings + FAISS vector search

·       RAG system design & implementation

·       Streamlit UI development

·       Full LLM app development & deployment

💰 Investment & Guarantee

Course Investment:

Regular Price: $797 🦜 Early Bird Special - Save $300

Register by Dec 25th: $497 Save $300 off regular price

Risk-Free Guarantee

"Build AI Apps or Your Money Back"

Complete the course and if you don't feel confident building AI applications, we'll refund every penny. No questions asked.

📅 Course Details

Schedule:

• Starts: January 29, 2026
• When: Thursdays, 6:00-7:30 PM EST
• Duration: 7 weeks (90 minutes each)
• Format: Live online via Zoom
• Class Size: Maximum 20 students

What You Get

Complete Learning Package: ✓ 6 live interactive sessions with expert instruction ✓ All sessions recorded - Lifetime access if you miss any ✓ Step-by-step workbook with all exercises ✓ Code templates for every project ✓ Private Discord community - Get help 24/7

Valuable Inclusions: ✓ OpenAI API setup guide - We'll help you get API key for use in class ✓ Professional portfolio creation - Show off your work ✓ Career guidance - How to showcase AI skills to employers ✓ Alumni network - Connect with other graduates

Frequently Asked Questions

“I’m not highly technical. Can I still succeed in this course?”
Absolutely. If you know basic Python (loops, functions) and can install packages, you can do this.
We teach everything step-by-step — from building your first chatbot to designing full RAG applications.

“Do I need machine learning or AI experience?”
No. This course does not require ML, deep learning, or advanced coding.
You’ll learn the architecture and workflow of modern LLM apps, which is very different from traditional ML.

“What if I miss a session?”
All sessions are recorded. You get lifetime access plus step-by-step instructions for every mini-project.
Our private Discord community provides help whenever you need it.

“Will this help me in my job or career?”
Yes — learners use these skills to automate workflows, build internal copilots, improve research/reporting, and create RAG applications that save teams hours of manual effort.
These are the exact skills organizations are hiring for.

“What if I fall behind?”
You won’t. The course is designed to build gradually:
first prompts → then memory → then RAG → then full application.
Recordings plus community support ensure you always catch up.

“Do I need to be great at coding?”
No. We'll walk you though step by step coding.

Ready to Build Real AI Applications?

Stop wondering, “Could I build something like that?”
Start saying, “Look what I built.”

The AI revolution is moving fast.
Every day you wait, your competitors and colleagues move ahead.

[ENROLL NOW — LIMITED TO 20 STUDENTS]

What Happens Next?

Once you enroll, you’ll receive:

• Immediate access to environment setup guides

• A welcome email with Discord invitation

• Your Week 1 preparation checklist

• A full access to syllabus

Email Support

We are deducate to help you succeed throughout your learning journey and beyond.