Diploma in Gen AI Engineering

What is Generative AI? – Definition and Concepts – Generative vs Discriminative Models – History and Evolution of Gen AI – Applications of Generative AI: Text Generation, Image Generation, Code Generation, Audio Generation. Large Language Models Overview – What are LLMs? – How LLMs Work: Transformer Architecture Basics – Training Process: Pre-training, Fine-tuning – Tokenization and Embeddings – Context Windows and Attention Mechanisms. LLM Capabilities and Limitations – What LLMs Can Do: Text Completion, Question Answering, Translation, Summarization – LLM Limitations: Hallucinations, Context Limits, Bias – When to Use LLMs – When Not to Use LLMs.

OpenAI Models – GPT-3.5, GPT-4 – Model Characteristics – Use Cases – API Access – Pricing and Limitations. Anthropic Models – Claude Models – Claude Characteristics – Use Cases – API Access – Comparison with GPT. Open Source Models – Llama Models (Meta) – Mistral Models – Model Characteristics – Local Deployment – Fine-tuning Capabilities. Other Model Families – Google Models: Gemini, PaLM – Cohere Models – Specialized Models: Code Models, Multimodal Models – Model Selection Criteria.

Transformer Architecture Overview – Attention Mechanism – Self-Attention – Multi-Head Attention – Positional Encoding – Encoder-Decoder Architecture. How LLMs Generate Text – Autoregressive Generation – Sampling Strategies: Greedy, Temperature, Top-k, Top-p – Token Prediction – Generation Parameters. Model Parameters and Scale – Parameter Counts – Model Sizes – Scaling Laws – Compute Requirements – Model Efficiency.

LLM APIs Overview – OpenAI API – Anthropic API – API Authentication – API Rate Limits – Cost Management. Basic API Usage – Making API Calls – Prompt Structure – Response Handling – Error Handling – Best Practices. Python Libraries for LLMs – OpenAI Python Library – Anthropic Python Library – LangChain Basics (Introduction) – Simple LLM Applications.

Text Generation Applications – Content Creation – Creative Writing – Code Generation – Documentation Generation – Use Case Examples. Question Answering Systems – Building Q&A Systems – Context Management – Answer Quality – Evaluation Metrics. Translation and Summarization – Machine Translation – Text Summarization – Extractive vs Abstractive Summarization – Implementation Approaches. Ethical Considerations – Bias in LLMs – Privacy Concerns – Misinformation – Responsible AI – Best Practices.

Introduction to Prompt Engineering – What is Prompt Engineering? – Why Prompt Engineering Matters – Elements of a Good Prompt – Prompt Structure: Instruction, Context, Examples, Output Format. Basic Prompting Techniques – Zero-Shot Prompting – One-Shot Prompting – Few-Shot Prompting – Prompt Templates – Prompt Variables – Iterative Prompt Refinement. Prompt Design Principles – Clarity and Specificity – Providing Context – Setting Constraints – Defining Output Format – Examples and Demonstrations – Common Mistakes.

Chain-of-Thought Prompting – What is Chain-of-Thought? – Step-by-Step Reasoning – Implementing CoT – CoT for Complex Problems – CoT Variations. Role-Based Prompting – Assigning Roles to LLMs – System Prompts – Persona-Based Prompts – Multi-Agent Scenarios – Role Consistency. Prompt Chaining – Breaking Complex Tasks into Steps – Sequential Prompts – Conditional Prompting – Prompt Pipelines – Managing State Across Prompts.

Few-Shot Learning Concepts – What is Few-Shot Learning? – In-Context Learning – Example Selection – Example Ordering – Few-Shot vs Fine-tuning. Implementing Few-Shot Learning – Selecting Good Examples – Formatting Examples – Providing Diverse Examples – Handling Edge Cases – Evaluating Few-Shot Performance. Advanced In-Context Learning – Dynamic Example Selection – Adaptive Few-Shot – Meta-Learning Concepts – Prompt Compression Techniques.

Introduction to Fine-tuning – What is Fine-tuning? – When to Fine-tune vs Prompt – Fine-tuning Process Overview – Data Requirements – Fine-tuning vs Pre-training. Fine-tuning Methods – Full Fine-tuning – Parameter-Efficient Fine-tuning (PEFT) – LoRA (Low-Rank Adaptation) – QLoRA (Quantized LoRA) – Adapter Methods. Fine-tuning Workflow – Data Preparation – Data Formatting – Training Configuration – Hyperparameter Tuning – Evaluation and Validation.

Fine-tuning Open Source Models – Fine-tuning Llama Models – Fine-tuning Mistral Models – Using Hugging Face Transformers – Training Infrastructure – Cost Considerations. Fine-tuning Best Practices – Data Quality – Data Augmentation – Overfitting Prevention – Evaluation Metrics – Model Selection – Deployment Considerations. Prompt Optimization – A/B Testing Prompts – Measuring Prompt Performance – Cost Optimization – Latency Optimization – Quality vs Cost Trade-offs.

OpenAI API Deep Dive – API Authentication – API Endpoints – Chat Completions API – Completions API – Embeddings API – Function Calling – Streaming Responses. Anthropic API – Claude API Overview – API Authentication – Message API – Streaming – Tool Use – API Best Practices. API Integration Patterns – Synchronous vs Asynchronous Calls – Error Handling – Retry Logic – Rate Limiting – Cost Management – Response Caching.

Introduction to Ollama – What is Ollama? – Why Use Local Models? – Ollama Installation – Ollama Architecture – Supported Models. Setting Up Ollama – Installation on Different Platforms – Downloading Models – Model Management – Running Models – Ollama CLI Usage. Ollama API – REST API Endpoints – Python Integration – Streaming Responses – Model Configuration – Performance Tuning.

Available Local Models – Llama Models – Mistral Models – Code Models – Multimodal Models – Model Selection Guide. Model Configuration – Context Window Settings – Temperature and Sampling – GPU vs CPU – Memory Management – Model Quantization. Performance Optimization – Hardware Requirements – GPU Acceleration – Model Quantization – Batch Processing – Optimization Techniques.

Trade-offs Analysis – Cost Comparison – Latency Comparison – Privacy Considerations – Customization – Scalability – Use Case Selection. Hybrid Approaches – Using APIs for Some Tasks – Using Local Models for Others – Fallback Strategies – Cost Optimization – Performance Optimization. Migration Strategies – Moving from API to Local – Moving from Local to API – Hybrid Deployment – Decision Framework.

Application Architecture – Designing LLM Applications – API Integration Patterns – Local Model Integration – Error Handling – Monitoring. Best Practices – Security Considerations – API Key Management – Rate Limiting – Cost Monitoring – Performance Monitoring – Logging and Debugging. Deployment Strategies – Deploying API-Based Applications – Deploying Local Model Applications – Containerization – Cloud Deployment – Edge Deployment.

Introduction to Embeddings – What are Embeddings? – Word Embeddings vs Sentence Embeddings – Embedding Models – Embedding Dimensions – Embedding Quality. Generating Embeddings – Using OpenAI Embeddings API – Using Open Source Embedding Models – Sentence Transformers – Embedding Generation Best Practices – Embedding Storage. Vector Similarity – Cosine Similarity – Euclidean Distance – Dot Product – Similarity Metrics Comparison – Choosing Similarity Metrics.

Introduction to Vector Databases – What are Vector Databases? – Vector Database vs Traditional Database – Use Cases for Vector Databases – Vector Database Architecture. Pinecone – Pinecone Overview – Setting Up Pinecone – Creating Indexes – Inserting Vectors – Querying Vectors – Pinecone Best Practices. Chroma – Chroma Overview – Installation and Setup – Creating Collections – Adding Documents – Querying – Chroma Features. Other Vector Databases – Weaviate – FAISS (Facebook AI Similarity Search) – Qdrant – Vector Database Comparison – Choosing Vector Database.

RAG Architecture – What is RAG? – Why RAG? – RAG Components: Retrieval, Augmentation, Generation – RAG Workflow – RAG vs Fine-tuning. Building RAG Systems – Document Processing – Text Chunking Strategies – Embedding Generation – Vector Storage – Retrieval Process – Context Assembly. LangChain RAG – LangChain RAG Components – Document Loaders – Text Splitters – Vector Stores – Retrievers – RAG Chains.

Document Processing – PDF Processing – Web Scraping – Document Parsing – Text Extraction – Metadata Extraction. Chunking Strategies – Fixed-size Chunking – Semantic Chunking – Recursive Chunking – Overlapping Chunks – Chunk Size Optimization. Retrieval Optimization – Hybrid Search: Keyword + Semantic – Re-ranking Results – Retrieval Strategies – Context Window Management – Retrieval Quality.

RAG Performance Optimization – Reducing Latency – Improving Accuracy – Cost Optimization – Caching Strategies – Batch Processing. RAG Evaluation – Retrieval Metrics – Generation Quality – End-to-End Evaluation – A/B Testing RAG Systems – Evaluation Frameworks. Production RAG Systems – Scalability Considerations – Monitoring RAG Systems – Error Handling – Version Control – Best Practices.

Introduction to AI Agents – What are AI Agents? – Agent vs LLM – Agent Architecture – Agent Components: Tools, Memory, Planning – Types of Agents: ReAct, Plan-and-Execute, Multi-Agent. Agent Capabilities – Tool Use – Function Calling – Web Search – Code Execution – Database Access – API Integration – Agent Limitations. Agent Design Patterns – Single Agent Systems – Multi-Agent Systems – Agent Hierarchies – Agent Collaboration – Agent Communication.

LangChain Overview – What is LangChain? – LangChain Components – Agents Module – Tools Module – Memory Module – Chains Module. Building Agents with LangChain – Agent Types: Zero-shot, ReAct, Plan-and-Execute – Creating Agents – Agent Tools – Agent Memory – Agent Execution. LangChain Tools – Built-in Tools – Custom Tools – Tool Wrappers – Tool Selection – Tool Execution – Error Handling.

Agent Orchestration – Complex Agent Workflows – Sequential Agent Execution – Parallel Agent Execution – Conditional Logic – State Management. Agent Memory Systems – Conversation Memory – Buffer Memory – Summary Memory – Entity Memory – Memory Optimization. Custom Agent Development – Building Custom Agents – Custom Tools – Custom Chains – Agent Extensions – Best Practices.

CrewAI Introduction – What is CrewAI? – CrewAI Architecture – Agents in CrewAI – Tasks in CrewAI – Crews in CrewAI – CrewAI vs LangChain. Building Multi-Agent Systems – Defining Agents – Creating Tasks – Forming Crews – Agent Roles – Task Assignment – Agent Collaboration. CrewAI Features – Agent Roles and Goals – Task Dependencies – Process Management – Output Parsing – CrewAI Best Practices.

Agent Monitoring and Debugging – Monitoring Agent Execution – Debugging Agents – Logging Agent Actions – Performance Metrics – Error Tracking. Agent Optimization – Reducing Latency – Cost Optimization – Improving Accuracy – Caching Strategies – Batch Processing. Deploying Agent Systems – Deployment Strategies – Containerization – Cloud Deployment – API Endpoints – Scaling Agents – Security Considerations.

Production System Requirements – Scalability – Reliability – Performance – Cost Management – Security – Monitoring – Error Handling. GenAI System Architecture – API Gateway – Load Balancing – Caching Strategies – Rate Limiting – Fallback Mechanisms – Circuit Breakers. Deployment Patterns – Serverless Deployment – Container Deployment – Kubernetes Deployment – Edge Deployment – Hybrid Deployment.

Introduction to MCP – What is MCP? – Why MCP? – MCP Architecture – MCP Components – MCP Use Cases. MCP Implementation – Setting Up MCP – MCP Servers – MCP Clients – Context Management – Model Integration. MCP Best Practices – Context Organization – Context Retrieval – Context Updates – Performance Optimization – Security Considerations.

GenAI Monitoring – Monitoring Metrics – Latency Monitoring – Cost Monitoring – Quality Monitoring – Error Monitoring – User Feedback. Evaluation Frameworks – Evaluation Metrics – Human Evaluation – Automated Evaluation – A/B Testing – Evaluation Best Practices. Quality Assurance – Input Validation – Output Validation – Quality Checks – Bias Detection – Safety Checks.

Error Handling Strategies – Retry Logic – Fallback Mechanisms – Graceful Degradation – Error Recovery – Error Notifications. Reliability Patterns – Redundancy – Failover – Health Checks – Circuit Breakers – Timeout Handling. Testing GenAI Systems – Unit Testing – Integration Testing – End-to-End Testing – Load Testing – Test Data Management.

Scaling Strategies – Horizontal Scaling – Vertical Scaling – Auto-scaling – Load Distribution – Resource Management. Performance Optimization – Latency Optimization – Throughput Optimization – Cost Optimization – Caching Strategies – Batch Processing. Production Best Practices – Security Best Practices – Compliance Considerations – Documentation – Version Control – Rollback Strategies.