QuantaText: From Prompts to Production
Bridging Theory with Real-World Practice for 15+ Years
Course Overview
Master AI Engineering with our comprehensive 12-week program structured into 3 progressive months. From foundational concepts to production-ready applications, learn to build autonomous AI agents and multimodal systems with clear weekly objectives and practical deliverables.
100+
Students
4.8/5
Rating
Hands-on
Projects
Certified
Engineer
Month 1: AI Foundations & Core Concepts
Weeks 1-4 • Building Strong Fundamentals
AI Engineering Fundamentals
Learning Goal: Understand AI landscape and your learning path
Topics Covered:
- Who is this course for? Prerequisites and career paths
- AI ecosystem overview and 2025 trends
- Setting up development environment (Python, Docker)
Deliverable: Personal learning plan + environment setup
Resources: 4 modules
Lab: First AI "Hello World"
Understanding Vectors & Embeddings
Learning Goal: Master the foundation of modern AI
Topics Covered:
- Vector embeddings (restaurant menu analogy)
- Vector databases and similarity search
- Practical vector operations
Deliverable: Vector search mini-project
Resources: 5 modules
Lab: Build semantic search engine
Large Language Models Demystified
Learning Goal: Understand LLM architecture without complex math
Topics Covered:
- What are LLMs? Core concepts simplified
- Transformer architecture overview
- NEW: Multimodal AI integration (2025 trend)
Deliverable: LLM concept map and presentation
Resources: 6 modules
Lab: Interact with different LLM APIs
LLM Internals & Optimization
Learning Goal: Deep dive into how LLMs actually work
Topics Covered:
- Transformer architecture deep dive
- NEW: Small Language Models (SLMs) vs LLMs
- Quantization and pruning techniques
- NEW: AI-Ready Data Management & Inference Optimization
Deliverable: LLM performance comparison analysis
Resources: 7 modules
Lab: Model optimization experiment
Month 2: LLM Engineering & Applications
Weeks 5-8 • From Theory to Practice
LLM Tradeoffs & Ethics
Learning Goal: Make informed decisions about LLM deployment
Topics Covered:
- Speed vs accuracy vs cost analysis
- NEW: AI Ethics, Responsible AI (TRiSM)
- NEW: Hallucination Detection & Mitigation
Deliverable: LLM deployment strategy document
Resources: 5 modules
Lab: Build hallucination detector
Advanced Reasoning & Prompting
Learning Goal: Master sophisticated AI reasoning techniques
Topics Covered:
- Advanced reasoning capabilities in LLMs
- NEW: Chain-of-Thought and Tree-of-Thought Prompting
- Systematic prompt engineering methodology
- NEW: Dynamic prompt generation
Deliverable: Advanced prompting toolkit
Resources: 8 modules
Lab: Reasoning AI assistant
RAG Systems & Knowledge Integration
Learning Goal: Build intelligent knowledge retrieval systems
Topics Covered:
- Building LLM workflows and intro to RAG
- Advanced retrieval techniques and hybrid search
- NEW: Multimodal RAG & Graph RAG with Knowledge Graphs
Deliverable: Enterprise RAG system prototype
Resources: 6 modules
Lab: Company knowledge assistant
Fine-tuning & Model Customization
Learning Goal: Customize models for specific domains
Topics Covered:
- Custom model training and adaptation
- NEW: Parameter-Efficient Fine-tuning (PEFT) & LoRA techniques
- Fine-tuning vs RAG decision framework
- Function calling and tool integration
Deliverable: Fine-tuned domain-specific model
Resources: 7 modules
Lab: Specialized AI agent
Month 3: Production AI & Agentic Systems
Weeks 9-12 • Scaling to Real-World Applications
AI Architecture & MLOps
Learning Goal: Design scalable AI systems for production
Topics Covered:
- Scalable system architecture and MLOps
- NEW: AI-Native Software Engineering principles
- NEW: Next-Generation Architectures & Mixture of Experts
- Deployment strategies (AWS, Google Cloud, Azure)
Deliverable: Production AI architecture blueprint
Resources: 9 modules
Lab: Scalable AI platform design
Introduction to AI Agents
Learning Goal: Build autonomous AI agents
Topics Covered:
- From chatbots to autonomous AI agents
- NEW: LangChain, LlamaIndex, CrewAI frameworks
- NEW: Model Context Protocol (MCP) - "USB-C of AI"
Deliverable: Basic autonomous agent
Resources: 6 modules
Lab: Task automation agent
Advanced Agent Systems
Learning Goal: Create sophisticated multi-agent systems
Topics Covered:
- Modern agent architectures and design patterns
- NEW: Tool use, external system integration & human-in-the-loop
- NEW: Multimodal AI Systems (Text, Vision, Audio, Video)
Deliverable: Multi-modal agent system
Resources: 8 modules
Lab: Custom agent framework
Enterprise AI & Capstone
Learning Goal: Deploy production-ready AI solutions
Topics Covered:
- Coordinated multi-agent systems and swarm intelligence
- NEW: Enterprise agent ecosystems & conflict resolution
- Complete AI engineering solution integration
Deliverable: Capstone Project - End-to-end AI platform
Resources: 5 modules
Lab: Industry showcase
Technology Stack Progression
Month 1:
Python, OpenAI/Anthropic APIs, Vector DBs (Pinecone, Weaviate)
Month 2:
LangChain, LlamaIndex, Hugging Face, Advanced RAG
Month 3:
CrewAI, MLOps (MLflow, W&B), Cloud Deployment
Production:
Docker, Kubernetes, AWS/GCP/Azure, Monitoring
Assessment & Certification
Continuous Assessment
Weekly labs and practical exercises
Monthly Projects
Month 1: Vector systems, Month 2: RAG assistant, Month 3: Multi-agent platform
Final Capstone
Complete AI engineering solution with industry presentation
Career Outcomes
Target Roles
- AI Engineer$85k-$150k
- ML Engineer$90k-$160k
- AI Product Manager$100k-$180k
Applications
- Enterprise reasoning systems
- Autonomous AI agents
- Multimodal AI experiences
*Price excluding GST
Final Price: ₹29,500 (incl. 18% GST)
What's Included:
- ₹3,000 worth of GPU Credits
- Live coding sessions
- Industry environments (Python, Docker)
- Career guidance & industry showcase
- Lifetime access to materials
- Certificate of completion
Assessment Structure:
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