Enterprise Generative AI and SDLC Optimization

Day 1: Foundations of Generative AI & Transformer Architecture

Module 1: Evolution of Generative AI

• Discriminative vs. Generative models

• Historical evolution: GPT, BERT, LLaMA

• Scaling laws and emergent behavior

• Open-source vs. proprietary LLM ecosystems

• Overview of Azure OpenAI Service and OpenAI APIs

Module 2: Transformer Internals & LLM Mechanics

• Tokenization strategies (BPE, SentencePiece)

• Embeddings and vector spaces

• Self-attention and multi-head attention

• Positional encoding

• Training vs. inference pipeline

Module 3: Hands-On Lab

• Text generation experiments

• Sentiment analysis on retail review dataset

• Temperature and decoding strategy comparison

Day 2: Multimodal Models & Advanced Prompt Engineering

Module 4: Multimodal LLMs

• Text + Image architectures

• Vision connectors and embeddings

• CLIP-style retrieval concepts

• Enterprise multimodal use cases

Module 5: Advanced Prompt Engineering

• Zero-shot and Few-shot prompting

• In-context learning

• Chain-of-Thought (CoT)

• Tree-of-Thought (ToT)

• Self-consistency and Reflexion

• LLM-as-a-judge frameworks

Module 6: Guardrails & Prompt Security

• System prompts and safety boundaries

• Prompt injection attacks

• Jailbreak mitigation strategies

• Output validation patterns

Module 7: Hands-On Lab

• Designing structured reasoning prompts

• Implementing guardrails

• Multimodal classification experiment

Day 3: Retrieval-Augmented Generation & AI Assistants

Module 8: RAG Architecture Design

• Naïve vs. Advanced RAG

• Chunking strategies

• Embedding model selection

• Hybrid search (keyword + vector)

• Hallucination mitigation

Module 9: Vector Databases & Retrieval Systems

• FAISS fundamentals

• Pinecone architecture

• Azure vector indexing patterns

• Latency vs. accuracy trade-offs

Module 10: Application Engineering with LangChain

• Prompt templates

• Sequential and Router chains

• Memory and chat history management

• Tool integration patterns

Module 11: Hands-On Lab

• Build RAG chatbot

• Query enterprise PDF documents

• Evaluate factual accuracy

Day 4: Multi-Agent Systems & Model Customization

Module 12: Agentic AI Foundations

• ReAct framework

• Tool usage lifecycle

• Autonomous vs. supervised agents

• Web Search and SQL agents

Module 13: Multi-Agent Orchestration

• Role-based agents (Researcher, Analyst, Writer)

• Collaborative reasoning workflows

• CrewAI orchestration model

• CrewAI vs. LangChain comparison

Module 14: Fine-Tuning & Customization

• RAG vs. Fine-tuning decision framework

• Lightweight fine-tuning workflows

• Dataset preparation and evaluation

• Domain adaptation strategies

Module 15: Hands-On Lab

• Implement multi-agent workflow

• Fine-tune lightweight GPT variant

• Compare base vs. tuned outputs

Day 5: Deployment, Security & Responsible AI

Module 16: Production Deployment Strategies

• Local UI development (Gradio / Streamlit)

• API-first microservices architecture

• Containerization and scaling strategy

• Observability and tracing

Module 17: Security & Governance

• Prompt injection testing

• Data privacy and PII masking

• Rate limiting and abuse monitoring

• AI governance frameworks

Module 18: Responsible AI & Evaluation

• Bias detection techniques

• Fairness metrics

• LLM evaluation benchmarks

• Human-in-the-loop validation

Module 19: Hands-On Lab

• Build secure chatbot interface

• Integrate OpenWeather API via function calling

• Conduct injection resilience testing

Day 6: AI-Driven SDLC Transformation

Module 20: AI in Requirements & Analysis

• NLP for requirement extraction

• Automated user story generation

• Risk prediction from historical data

Module 21: AI-Assisted Design

• Pattern recognition for architecture

• AI-driven UML and wireframe suggestions

• Using Figma AI workflows

Module 22: AI-Powered Development

• Pair programming with GitHub Copilot

• Code review automation

• Bug detection and static analysis with Amazon Q

Module 23: Testing & Quality Engineering

• Automated test case generation

• Predictive coverage analytics

• AI-driven regression prioritization

Module 24: CI/CD & Maintenance Optimization

• AI-based pipeline monitoring

• Incident root-cause analysis

• Log summarization and anomaly detection

• User feedback sentiment analysis

Module 25: Hands-On Lab

• Generate automated test cases

• Optimize CI/CD workflow with AI assistant

• Build AI-powered bug triage workflow

Upon completion, participants will be able to:

• Engineer production-ready RAG and multi-agent systems
  • Select between fine-tuning and retrieval architectures
  • Secure LLM systems against prompt injection
  • Deploy scalable, observable AI microservices
  • Integrate AI across SDLC phases for measurable efficiency

Measurable Enterprise Outcomes:

• 30–50% acceleration in prototyping cycles
  • Reduced hallucination rates through structured RAG design
  • Improved code quality via AI-assisted development
  • Faster defect detection and resolution cycles
  • Standardized enterprise LLM governance blueprint