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From Fundamentals to Scalability

GenAI Mastery: LLM Orchestration &
AI Engineering

Become an Expert in Next-Gen AI Technologies - Master LLM orchestration with LangChain, CrewAI, MCP, prompt engineering, AI agent development, and production GenAI systems

Overview Curriculum Projects FAQ

📚

What You'll Master

Productionalized GenAI use cases, LLM orchestration, MCP implementations, AI agent systems, RAG, and scalable deployment

Start Here 📖

12-Week Curriculum

Structured learning path from LLM fundamentals to production deployment

12 Weeks 🚀

Capstone Projects

Enterprise AI Assistant, NLP-to-SQL Multi-Agent System, Custom Domain AI

3 Projects ❓

Frequently Asked Questions

Everything you need to know about the GenAI Mastery bootcamp

FAQ

What You'll Master

Transform from a curious AI enthusiast into a production-ready AI Engineer

🎯 LLM Fundamentals

Master prompt engineering, model behaviors, and optimization techniques for maximum effectiveness

🔗 LangChain & LangGraph

Build complex AI workflows, chains, and agents with industry-standard frameworks

🤖 AI Agent Development

Create autonomous agents that can reason, use tools, and solve complex problems

🧠 Multi-Agent Systems

Orchestrate teams of AI agents working collaboratively with CrewAI

📊 Vector Databases & RAG

Build retrieval-augmented generation systems with Pinecone, Weaviate, ChromaDB

🚀 Production Deployment

Deploy AI systems with monitoring, evaluation, and scaling strategies

12 Weeks Intensive

3 Portfolio Projects

24/7 Support Available

100% Production-Ready

12-Week Intensive Curriculum

Structured learning path from foundations to production deployment

Phase 1: Foundations (Weeks 1-3)

Week 1: LLM Fundamentals & Prompt Engineering

How LLMs work - tokenization, embeddings, attention mechanisms
OpenAI API, Anthropic Claude API, Google Gemini integration
Prompt engineering techniques - zero-shot, few-shot, chain-of-thought
Temperature, top-p, and parameter optimization
Token optimization and cost management

📝 Assignment: Build a prompt testing framework that compares responses from GPT-4, Claude, and Gemini for the same prompt. Analyze differences in output quality, token usage, and cost.

📊 Quiz: LLM fundamentals, tokenization concepts, API parameters, prompt engineering techniques, cost optimization strategies

Week 2: Advanced Prompting & Context Management

System prompts and role definitions
Context window management and chunking strategies
Structured output generation (JSON, XML)
Model Context Protocol (MCP) fundamentals
Handling long documents and conversations

📝 Assignment: Create a document Q&A system using MCP for context management. Implement intelligent chunking for a 50+ page PDF and generate structured JSON responses with citations.

📊 Quiz: System prompts, context window limits, MCP concepts, chunking strategies, structured output formats

Week 3: Embeddings & Vector Databases

Understanding embeddings and semantic search
Vector databases - Pinecone, Weaviate, ChromaDB, FAISS
Similarity search algorithms
Metadata filtering and hybrid search
Building knowledge bases

📝 Assignment: Build a semantic search engine for technical documentation. Index 100+ documents in Pinecone, implement metadata filtering, and compare cosine vs. euclidean similarity. Include a search analytics dashboard.

📊 Quiz: Embedding concepts, vector database comparisons, similarity metrics, indexing strategies, hybrid search implementations

Phase 2: LLM Orchestration Frameworks (Weeks 4-6)

Week 4: LangChain Foundations

LangChain architecture and components
Chains - LLMChain, SequentialChain, RouterChain
Memory types - ConversationBufferMemory, ConversationSummaryMemory
Document loaders and text splitters
Output parsers and structured responses

📝 Assignment: Build a conversational chatbot with LangChain that maintains context across 10+ turns. Implement multiple memory types and compare their effectiveness. Add conversation summarization for long chats.

📊 Quiz: LangChain architecture, chain types, memory mechanisms, document loaders, output parsers, best practices

Week 5: Advanced LangChain & RAG Systems

Retrieval-Augmented Generation (RAG) patterns
Vector store integrations
Multi-query retrievers and parent document retrievers
ReAct and Self-Ask patterns
Callback handlers and logging

📝 Assignment: Create an enterprise knowledge base using RAG architecture. Ingest 200+ company documents, implement multi-query retrieval, add source attribution, and build evaluation metrics for answer quality.

📊 Quiz: RAG architecture patterns, retriever types, ReAct vs Self-Ask, callback mechanisms, evaluation metrics

Week 6: LangGraph & Complex Workflows

State machines and graph-based workflows
LangGraph fundamentals - nodes, edges, state
Conditional branching and loops
Human-in-the-loop patterns
Subgraphs and modularity

📝 Assignment: Build a multi-step research assistant using LangGraph. Implement conditional branching based on query complexity, add human-in-the-loop approval for critical decisions, and visualize the execution graph.

📊 Quiz: State machine concepts, LangGraph architecture, node/edge definitions, branching logic, HITL patterns, debugging workflows

Phase 3: AI Agents & Multi-Agent Systems (Weeks 7-9)

Week 7: Building AI Agents

Agent architecture and reasoning loops
Tool calling and function invocation
ReAct agents, Plan-and-Execute agents
Custom tools development
Agent memory and state management

📝 Assignment: Create a data analysis agent with custom pandas tools. Agent should autonomously load CSV files, perform exploratory analysis, detect anomalies, generate visualizations, and provide insights. Include error handling and retry logic.

📊 Quiz: Agent architectures, reasoning patterns, tool calling mechanisms, ReAct vs Plan-and-Execute, custom tool development

Week 8: CrewAI & Agent Orchestration

CrewAI framework fundamentals and architecture
Defining roles, goals, and backstories
Task delegation and collaboration patterns
Sequential vs. parallel execution
Agent communication protocols
Building multi-agent orchestrations with CrewAI
Real-world crew implementations - research teams, content pipelines

📝 Assignment: Build a content creation crew with 3 agents (Researcher, Writer, Editor). Implement both sequential and parallel execution modes. Add quality checks and revision loops. Document agent interactions and decision-making process.

📊 Quiz: CrewAI architecture, agent roles/goals, task delegation, execution modes, collaboration patterns, orchestration strategies

Week 9: Advanced Orchestration & n8n Integration

Hierarchical agent structures
Consensus mechanisms and voting
n8n workflow automation fundamentals
Building AI orchestrations with n8n + LLMs
Integrating CrewAI agents with n8n workflows
Event-driven agent triggering and coordination
Agent monitoring and debugging
Error handling and fallback strategies

📝 Assignment: Create an autonomous customer support system using n8n + CrewAI. Build workflows for ticket classification, agent routing, escalation rules, and response generation. Integrate with email/Slack. Implement monitoring dashboard with success metrics.

📊 Quiz: n8n workflow concepts, LLM integration patterns, CrewAI + n8n orchestration, event-driven triggers, error handling, monitoring strategies

Phase 4: Production & Deployment (Weeks 10-12)

Week 10: Evaluation & Monitoring

LLM evaluation frameworks (RAGAS, LangSmith)
Metrics - relevance, faithfulness, context precision
A/B testing for prompts and models
Logging and tracing with LangSmith/Phoenix
Cost tracking and optimization

📝 Assignment: Build a comprehensive evaluation pipeline for your RAG system from Week 5. Implement RAGAS metrics, create A/B testing framework for 3 different retrieval strategies, set up LangSmith tracing, and generate a detailed performance report with cost analysis.

📊 Quiz: Evaluation frameworks, RAG metrics, A/B testing methodologies, tracing/logging tools, cost optimization techniques

Week 11: Fine-tuning & Model Optimization

When to fine-tune vs. prompt engineering
Fine-tuning OpenAI models
LoRA and QLoRA for efficient fine-tuning
Dataset preparation and validation
Supervised fine-tuning (SFT) workflows

📝 Assignment: Fine-tune a GPT-3.5 model for a domain-specific task (choose legal, medical, or financial). Prepare 500+ training examples, implement validation splits, fine-tune the model, and benchmark against base model. Document when fine-tuning is worth the cost vs. few-shot prompting.

📊 Quiz: Fine-tuning decision criteria, LoRA/QLoRA concepts, dataset preparation, validation techniques, cost-benefit analysis

Week 12: Production Deployment

API design for LLM applications
Caching strategies (semantic caching)
Rate limiting and queue management
Deployment on AWS/GCP/Azure
Docker containerization and streaming responses

📝 Assignment: Deploy your Enterprise AI Assistant (from capstone project) to production. Containerize with Docker, set up CI/CD pipeline, implement semantic caching, add rate limiting, deploy to cloud (AWS/GCP), set up monitoring/alerts, and load test with 1000+ concurrent requests. Document your deployment architecture.

📊 Quiz: API design patterns, caching strategies, rate limiting techniques, cloud deployment, Docker/Kubernetes, monitoring/alerting, scaling strategies

Weeks 1-3: Programming 101 (Phase 1)

Week 1

Day 1: Variables, types, operators
Day 2: Control flow & boolean logic
Day 3: Loops & iteration patterns
Day 4: Functions/methods, scope & returns
Day 5: I/O & core collections (list/array/dict)

Assignment: CLI calculator (both languages). Quiz: 10 MCQs + 1 coding.

Week 2

Day 1: OOP basics (classes/objects)
Day 2: Encapsulation, inheritance, polymorphism
Day 3: Exceptions & error handling
Day 4: File I/O in Java & Python
Day 5: OOP workshop (build a mini library)

Assignment: Book Library class with save/load. Quiz: 10 MCQs + 1 coding.

Week 3

Day 1: Modular programming & packaging
Day 2: Complexity & Big-O
Day 3: Debugging & unit testing (JUnit/pytest)
Day 4: Git/GitHub basics; PR etiquette
Day 5: Mini-project: CLI To-Do app (CRUD)

Assignment: To-Do app with tests & README. Quiz: 10 MCQs + 1 coding.

Weeks 4-6: Data Structures (Phase 2)

Week 4

Day 1: Arrays & lists
Day 2: Linked lists (SLL/DLL)
Day 3: Stacks (LIFO) & use-cases
Day 4: Queues/Deque & variants
Day 5: Lab: implement DS in both languages

Assignment: Implement List/Stack/Queue APIs. Quiz: 10 MCQs + 1 coding.

Week 5

Day 1: Trees & recursion basics
Day 2: BST ops (insert/delete/search)
Day 3: Graphs & adjacency models
Day 4: Traversals: DFS/BFS
Day 5: Workshop: paths, levels, cycles

Assignment: BST + BFS on grid; README with Big-O. Quiz: 10 MCQs + 1 coding.

Week 6

Day 1: Hash tables (hashing, collisions)
Day 2: Heaps/PQs; heap sort
Day 3: Recursion deep-dive
Day 4: Memory mgmt, GC concepts
Day 5: DS practice set (mixed)

Assignment: LRU Cache (hashmap+DLL). Quiz: 10 MCQs + 1 coding.

Weeks 7-9: Problem Solving (Phase 3)

Focus on patterns and 30-40 easy/medium LeetCode-style questions with guided walkthroughs. Daily flow: short lecture goals → key takeaways → real-world analogy → hands-on exercise → stretch → review.

Week 7

Day 1: Problem analysis & constraints; pattern library intro
Day 2: Two-pointers; sorted arrays & string scans
Day 3: Sliding window (fixed & variable)
Day 4: Sorting fundamentals; stability & when to use what
Day 5: Review set (6-8 questions) + live walkthrough

Assignment: 10 questions (2× two-pointers, 4× sliding-window, 4× sorting). Quiz: 10 MCQs + 1 coding.

Week 8

Day 1: Recursion patterns & backtracking (subsets, permutations)
Day 2: Dynamic Programming I (memoization vs tabulation)
Day 3: DP II (knapsack, coin change, LIS ideas)
Day 4: Graph algorithms I (BFS shortest path, topo sort)
Day 5: Mock interview #1 (15-min DSA + 10-min feedback per student)

Assignment: 10 questions (3× recursion/backtracking, 5× DP, 2× graph BFS). Quiz: 10 MCQs + 1 coding.

Week 9

Day 1: Greedy techniques; exchange arguments & proofs of correctness (informal)
Day 2: Advanced graphs (Dijkstra intro; when BFS vs Dijkstra)
Day 3: Mixed set (hashing, heap, prefix sum)
Day 4: System-aware problem solving (I/O limits, memory caps)
Day 5: Mock interview #2 + feedback & personalized plan

Assignment: 10 questions (2× greedy, 3× heap, 3× hashing/prefix, 2× graph). Quiz: 10 MCQs + 1 coding.

Weeks 10-11: Databases (Phase 4)

Week 10

Day 1: Relational model, tables, PK/FK; ER → schema
Day 2: Normalization (1NF-3NF); denormalization trade-offs
Day 3: SELECT, WHERE, ORDER BY, LIMIT; CRUD basics
Day 4: Joins (INNER/LEFT/RIGHT/FULL), GROUP BY, HAVING
Day 5: Lab: design a Course–Student–Enrollment schema

Assignment: Create schema & seed data; 12 queries (mix of joins & groups). Quiz: 12 MCQs + 1 query.

Week 11

Day 1: Indexes & query plans; when indexes hurt/help
Day 2: Transactions, ACID; isolation levels & anomalies
Day 3: Stored routines & views (intro), pagination patterns
Day 4: NoSQL overview (key-value, document); when to choose which
Day 5: Mini-project: Analytics queries & simple dashboard export (CSV/JSON)

Assignment: Optimize queries (add/remove indexes), measure timings, document rationale. Quiz: 12 MCQs + 1 query.

Weeks 12-13: System Design (Phase 5)

Week 12

Day 1: Client-server, REST, HTTP verbs, idempotency; API design (resources, pagination)
Day 2: Statelessness, session vs token auth (concepts); rate limiting basics
Day 3: Caching (CDN, reverse proxy, app-level); cache invalidation strategies
Day 4: Load balancing (round-robin, least-conn); health checks; blue/green overview
Day 5: Design exercise: URL Shortener (read-heavy, cache, DB schema, API)

Assignment: Write a 2-page design doc + simple API spec (OpenAPI snippet encouraged). Quiz: 12 MCQs.

Week 13

Day 1: Databases at scale: replication vs sharding; read/write paths
Day 2: Consistency models; CAP & PACELC intuition; queues for decoupling
Day 3: Observability 101 (logs, metrics, traces) & SLOs; error budgets
Day 4: Design exercise: News Feed/Timeline (fan-out, denorm, caches)
Day 5: Mock system design interview + structured feedback rubric

Assignment: 3-page design doc with diagram and capacity estimates (QPS, storage). Quiz: 12 MCQs.

Week 14: Gen AI & AI Tools (Phase 6)

Week 14

Day 1: GitHub Copilot fundamentals: Installation, configuration, code generation
Day 2: ChatGPT for development: Prompt engineering, debugging, documentation
Day 3: Claude & advanced AI: Code analysis, architecture suggestions, optimization
Day 4: AI debugging tools: Error analysis, security scanning, performance optimization
Day 5: Modern AI workflow: CI/CD integration, AI ethics & best practices

Assignment: Enhance capstone project using AI tools. Quiz: 10 MCQs on AI tool usage.

Week 15: Capstone Project (Phase 7)

Build 2-3 complete end-to-end applications that combine API + Database + Frontend concepts with AI enhancement. Teams of 2-3; PR-based workflow on GitHub.

Project 1: Smart To-Do with Analytics (REST API, MySQL, dashboard)
Project 2: E-Commerce Lite (Catalog, cart, orders, inventory consistency)
Project 3: Minimal Chat Service (User/channel models, message APIs, pagination)

Deliverables: Design doc (3-5 pages), API spec (OpenAPI), DB schema (ER + DDL), runnable code, README, demo video (≤5 min).

🚀 Game-Changing Capstone Projects

Build production-grade AI applications that will make recruiters stop scrolling

      💼 These aren't toy projects! Each capstone uses cutting-edge MCP, multi-agent orchestration, and real enterprise patterns that companies are actively hiring for.
    

🏢 Project 1: Enterprise AI Assistant with MCP

The Ultimate Intelligent Workplace Copilot

🎯 Why This Project Stands Out:

This is THE project that demonstrates you can build production enterprise AI systems. Companies are desperately seeking engineers who can implement Model Context Protocol for standardized, scalable AI applications.

🔗 Model Context Protocol (MCP) LangChain Claude/GPT-4 Pinecone FastAPI

✨ Advanced Features You'll Build

🧠 Multi-Document RAG with MCP: Context-aware retrieval across enterprise knowledge base
💬 Conversational Memory: Natural dialogue with full conversation history
📊 Source Attribution: Transparent citations with confidence scores
🎨 Admin Dashboard: Analytics, usage metrics, and cost tracking
🔐 Access Control: Role-based permissions and data security
⚡ Real-time Streaming: Token-by-token response generation

🎓 What You'll Master

Implementing standardized MCP for context management
Building production RAG systems with error handling
Deploying scalable AI APIs with monitoring
Cost optimization and caching strategies

💼 Career Impact: This single project has helped students land $120K+ AI Engineer roles. Recruiters LOVE seeing MCP implementation!

🗣️💾 Project 2: NLP-to-SQL Multi-Agent System

Talk to Your Database in Plain English

🎯 The Holy Grail Project:

This is what every data-driven company wants! A system where business users ask questions in natural language and AI agents handle the entire SQL workflow. You'll orchestrate multiple specialized agents using MCP for standardized communication.

🔗 MCP Multi-Agent CrewAI LangGraph Claude Sonnet 4 PostgreSQL/BigQuery

🤖 Your AI Agent Team

📝 Query Interpreter Agent: Understands user intent and breaks down complex questions
🔍 Schema Navigator Agent: Explores database structure and identifies relevant tables
⚙️ SQL Generator Agent: Crafts optimized SQL queries with CTEs and joins
✅ Validator Agent: Tests queries and ensures data integrity
📊 Result Analyzer Agent: Interprets results and generates insights
🎨 Visualization Agent: Creates charts and dashboards automatically

🔥 Advanced Orchestration Features

🔗 MCP-Powered Communication: Standardized agent-to-agent messaging
🔄 Dynamic Task Delegation: Intelligent work distribution
🧩 Query Decomposition: Break complex questions into sub-queries
💡 Self-Healing: Agents detect and fix SQL errors automatically
📈 Performance Optimization: Query rewriting for speed
🎯 Context Preservation: Multi-turn conversations with memory

🛠️ Real-World Scenarios You'll Handle

"Show me top 10 customers by revenue last quarter"
"Which products have declining sales trends?"
"Compare YoY growth across all regions"
"Find anomalies in our transaction data"

🌟 Industry Demand: NLP-to-SQL systems are projected to be a $2B+ market by 2026. Companies like Tableau, ThoughtSpot, and startups are scrambling to hire engineers with these skills!

🎯 Project 3: Custom Domain AI Powerhouse

Your Industry-Specialized AI Solution

🎯 Make It Yours:

Choose a domain you're passionate about and build a specialized AI system with fine-tuning, custom agents, and MCP integration. This is your showcase project that demonstrates deep expertise!

🎨 Fine-tuned Models 🔗 MCP Integration Custom Agents Cloud Deployment

🏆 Choose Your Domain & Build:

⚖️ Legal AI Assistant

Contract analysis and clause extraction
Precedent search with case citations
Risk assessment and compliance checking
MCP-based document workflow orchestration

🏥 Medical Diagnosis Support

Symptom analysis with medical literature
Drug interaction checking
Treatment protocol recommendations
Multi-agent diagnostic reasoning system

📈 Financial Report Analyzer

10-K/10-Q document extraction
Sentiment analysis on earnings calls
Competitive analysis automation
MCP-coordinated research agents

💻 Code Review & Debug Agent

Automated code quality assessment
Security vulnerability detection
Performance optimization suggestions
Multi-agent code improvement workflow

🔬 Advanced Techniques You'll Master

🎯 Domain-Specific Fine-tuning: Adapt models to your industry
📚 Custom Knowledge Integration: Inject specialized databases
🔗 MCP Standardization: Industry-standard context protocols
⚡ Production Deployment: Docker + Kubernetes + CI/CD
📊 Performance Benchmarking: Prove superiority over base models

💎 Portfolio Differentiator: While others show generic chatbots, you'll demonstrate deep domain expertise that commands premium salaries. Industry-specific AI engineers earn 40-60% more!

🎓 Full Support for Every Project

📝

Weekly Code Reviews

Get expert feedback on your implementation

👥

Office Hours

1-on-1 guidance when you're stuck

🚀

Deployment Help

Launch your projects to production

📹

Demo Recording

Create impressive presentation videos

Register for Free Webinar

What You'll Master

🎯 LLM Fundamentals

🔗 LangChain & LangGraph

🤖 AI Agent Development

🧠 Multi-Agent Systems

📊 Vector Databases & RAG

🚀 Production Deployment

12-Week Intensive Curriculum

Phase 1: Foundations (Weeks 1-3)

Phase 2: LLM Orchestration Frameworks (Weeks 4-6)

Phase 3: AI Agents & Multi-Agent Systems (Weeks 7-9)

Phase 4: Production & Deployment (Weeks 10-12)

Weeks 1-3: Programming 101 (Phase 1)

Week 1

Week 2

Week 3

Weeks 4-6: Data Structures (Phase 2)

Week 4

Week 5

Week 6

Weeks 7-9: Problem Solving (Phase 3)

Week 7

Week 8

Week 9

Weeks 10-11: Databases (Phase 4)

Week 10

Week 11

Weeks 12-13: System Design (Phase 5)

Week 12

Week 13

Week 14: Gen AI & AI Tools (Phase 6)

Week 14

Week 15: Capstone Project (Phase 7)

🚀 Game-Changing Capstone Projects

🏢 Project 1: Enterprise AI Assistant with MCP

✨ Advanced Features You'll Build

🎓 What You'll Master

🗣️💾 Project 2: NLP-to-SQL Multi-Agent System

🤖 Your AI Agent Team

🔥 Advanced Orchestration Features

🛠️ Real-World Scenarios You'll Handle

🎯 Project 3: Custom Domain AI Powerhouse

🏆 Choose Your Domain & Build:

🔬 Advanced Techniques You'll Master

🎓 Full Support for Every Project

Frequently Asked Questions

⚠️ The Job Market Harsh Reality

Ready to Master GenAI Engineering?