Advanced AI Engineering & Vibe Coding: Architecting Intelligent and Emotion-Aware Systems

Generative AI Paradigms

• Discriminative vs Generative AI
• Evolution of LLMs (GPT, BERT, T5, LLaMA)
• Tokenization to embeddings to generation
• Context windows and scaling considerations

Architecture discussion:
● Model scaling laws and compute trade-offs
● Foundation model selection strategies

Hands-on:
● Text generation using OpenAI Playground
● Sentiment analysis on Amazon Reviews dataset

Real-world application:
● Selecting appropriate models for enterprise workloads

Transformer Architecture Deep Dive

• Self-attention mechanisms
• Multi-head attention
• Feedforward layers
• Positional encoding
• Decoder-only vs encoder-decoder trade-offs

Hands-on:
● Visualizing attention weights
● Implement simplified transformer block

Multimodal Systems

• Vision-language model fundamentals
• CLIP architecture overview
• GPT-4V and DALL·E integrations
• Multimodal orchestration with LangChain

Hands-on:
● Build multimodal classifier
● Image + text retrieval system

Advanced Prompt Engineering

• Zero-shot vs few-shot prompting
  ● Chain-of-Thought (CoT)
  ● Tree-of-Thought (ToT)
  ● Reflexion strategies
  ● Guardrails and safety prompts

Hands-on:
● Develop Custom GPT
● Implement system-level safety instructions

Case study:
● Multimodal news classification system

LangChain Architecture

• Sequential chains
• Prompt templates
• Conversational memory
• Tool integration patterns

Hands-on:
● Build structured conversational assistant

Retrieval-Augmented Generation (RAG)

• RAG system architecture
• Chunking strategies
• Vector databases (FAISS, Pinecone, Chroma)
• Minimizing hallucinations
• Hybrid retrieval strategies

Hands-on:
● Build RAG chatbot (LangChain + FAISS)
● Azure-based RAG implementation

Case study:
● AI Q&A over Tesla annual reports

AI Agents & Tool Use

• ReAct framework
• LangChain agents
• SQL and search tool integration
• Error recovery strategies

Hands-on:
● Build tool-enabled agent

Multi-Agent Collaboration & Fine-Tuning

• CrewAI framework
• Role-based orchestration (Researcher, Analyst, Writer)
• Fine-tuning vs prompt optimization
• Parameter-efficient fine-tuning (LoRA/PEFT)
• Model benchmarking (GPT vs Claude vs LLaMA)

Hands-on:
● Fine-tune small dataset
● Compare baseline vs tuned outputs

Deployment Architectures

• API-driven deployment (Gradio, Streamlit)
• Cloud vs local hosting
• CI/CD for AI systems

Hands-on:
● Build chatbot UI using Gradio
● Integrate OpenWeather API via function calling

Security, Governance & Observability

• Prompt injection prevention
• OWASP Top 10 for LLM applications
• Data privacy and compliance
• LangChain tracing and observability
• Scaling strategies and monitoring

Case study:
● End-to-end chatbot deployment with injection safety validation

● From rule-based systems to emotion-sensitive AI
● Psychology of digital interaction
● Emotional intelligence in conversational systems

● Emotional variables and tone adaptation
● Contextual flow control
● Sentiment analysis using TextBlob and Hugging Face

• Working with GoEmotions dataset
• Emotion classification models
• Tone-adaptive response engine

Hands-on:
● Build mood-aware conversational assistant

• Emotion-aware customer service systems
• Adaptive marketing personalization
• Human-centered AI design frameworks
• Emerging trends in emotion-driven AI

Reflection workshop:
● Mapping emotion-aware systems to industry use cases

Capstone Project: Intelligent & Emotion-Aware AI System

Participants will:

● Build multi-agent RAG-enabled assistant
● Integrate emotion detection layer
● Deploy via API-driven interface
● Implement injection safety and governance
● Present architectural decisions and scalability strategy