QuantaText: From Prompts to Production

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)

Personal learning plan + environment setup

Master the foundation of modern AI

Topics Covered:

• •Vector embeddings (restaurant menu analogy)
• •Vector databases and similarity search
• •Practical vector operations

Vector search mini-project

Understand LLM architecture without complex math

Topics Covered:

• •What are LLMs? Core concepts simplified
• •Transformer architecture overview
• •NEW: Multimodal AI integration (2025 trend)

LLM concept map and presentation

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

LLM performance comparison analysis

Make informed decisions about LLM deployment

Topics Covered:

• •Speed vs accuracy vs cost analysis
• •NEW: AI Ethics, Responsible AI (TRiSM)
• •NEW: Hallucination Detection & Mitigation

LLM deployment strategy document

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

Advanced prompting toolkit

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

Enterprise RAG system prototype

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

Fine-tuned domain-specific model