Advanced AI Engineering & Vibe Coding
Overview
This integrated program provides a comprehensive journey into Large Language Models (LLMs), Multimodal AI, Retrieval-Augmented Generation (RAG), Multi-Agent Systems, and the emerging field of Vibe Coding. Participants will gain hands-on experience building real-world AI applications—from foundational transformer understanding to production-ready deployments and emotion-aware systems.
The curriculum is divided into two primary tracks: a deep technical dive into modern AI architectures and a specialized exploration of “Vibe Coding”—the art of building systems that respond to and express human emotion.
Audience
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AI / ML Engineers: Building and deploying LLMs, RAG systems, and multi-agent workflows.
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NLP Practitioners: Working with transformers, embeddings, and emotion-aware models.
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Data Scientists: Handling vector databases, multimodal datasets, and fine-tuning AI models.
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Software Developers: Interested in integrating AI into production-ready applications.
Prerequisites
- Technical: Python & Basic ML knowledge (functions, pandas, sklearn).
- NLP: Understanding of tokenization, embeddings, and transformers.
- Tools: Exposure to APIs, Azure cloud basics, and familiarity with Jupyter / VS Code / Colab.
Curriculum
Concepts Covered:
- Discriminative vs. Generative AI paradigms
- Progression of Large Language Models (GPT, BERT, T5, LLaMA)
- From tokenization to embeddings to text generation
Core Architecture:
- Self-attention mechanisms
- Multi-head attention
- Feedforward layers
- Positional encoding
Platform Landscape:
- Hugging Face vs. OpenAI vs. Azure OpenAI ecosystems
Practical Lab:
- Text generation using OpenAI Playground
Sentiment analysis on the Amazon Reviews dataset
Multimodal AI Overview:
- Text-and-image models (GPT-4V, DALL·E)
- CLIP fundamentals
- Vision integrations using LangChain
Prompt Engineering Mastery:
- Zero-shot & Few-shot prompting
- Chain-of-Thought (CoT)
- Tree-of-Thought (ToT)
- Reflexion strategies
Hands-on Workshop:
- Developing Custom GPTs
- Implementing system prompts and safety guardrails
LangChain in Practice:
- Sequential chains
- Prompt templates
- Managing conversational memory
Retrieval-Augmented Generation (RAG):
- System design and architecture
- Vector databases (FAISS, Pinecone, Chroma)
- Techniques to minimize hallucinations
Practical Lab:
- Build a RAG chatbot using LangChain + FAISS
- Implement RAG with Azure
AI Agents & Tool Use:
- LangChain agents
- ReAct framework for search and SQL execution
Multi-Agent Collaboration:
- CrewAI framework
- Role-based agent orchestration (Researcher, Analyst, Writer)
Fine-Tuning Strategies:
- When to fine-tune vs. optimize prompts
- Parameter-efficient fine-tuning (PEFT/LoRA)
- Model comparison (GPT vs. Claude vs. LLaMA)
Hands-on Workshop:
- Fine-tune using a small dataset
- Compare baseline vs. fine-tuned outputs
Deployment Approaches:
- API-driven applications (Gradio, Streamlit)
- Cloud deployment vs. local hosting
Security & Governance:
- Prompt injection prevention
- Data privacy considerations
- OWASP Top 10 for LLM applications
Scaling & Monitoring:
- LangChain tracing and observability
- Error handling strategies
Practical Lab:
- Build a chatbot UI using Gradio
- Integrate OpenWeather API via function calling
- Transition from rule-based logic to emotion-sensitive systems
- Understanding the psychology of digital interactions
- Emotional variables and tone adaptation
- Structuring conversational flow with contextual awareness
Hands-on:
- Tone detection using sentiment analysis tools (TextBlob / Hugging Face)
- Environment setup and working with emotion-labeled datasets (GoEmotions)
- Building a tone-adaptive response engine
- Happy → Cheerful
- Sad → Empathetic
- Happy → Cheerful
Mini Project:
- Develop a mood-aware conversational assistant
- Emotion-aware customer service systems
- Adaptive marketing and personalized communication
Reflection Exercise:
- Mapping vibe adaptation to specific business use cases
- Recap of key concepts
- Emerging trends in emotion-aware AI systems
