Comprehensive Generative AI for Developers Training

Course Overview

LLM Foundations

• Introduction to Generative AI for Software Development
• Generative Models and their Use Cases
• Transformer architecture and its impact on LLM performance
• LLM Training Process - pre-training, fine-tuning, and reinforcement learning
• Exploring Real-World LLM Applications

Speaking to LLMs: Prompt Engineering

• Prompt Engineering Introduction
• Techniques for creating effective prompts
• Zero-Shot Learning, Few-Shot, and Chain-of-Thought
• Prompt Engineering for Developers
• Leverage LLMs for code generation, completion, and analysis
• Best practices for prompt design and optimization in a development context
• Optimize prompting workflows for next-generation scripting
• Handle and process LLM-generated code
• Integrate prompts into development pipelines

Accessing LLMs via APIs

• Roles and Conversation Threading
• Popular LLMs, APIs, and Libraries - Generative AI Tech Stack
• LangChain for Integration
• Closed-Source LLMs vs Open-Source LLMs
• Chat Agents for Querying Developer Documentation via API

Enhancing LLMs with Fine-Tuning

• State of the Art Open-Source LLMs
• Building Pipelines with HuggingFace Transformers Library
• Fine-Tuning with the Hugging Face Transformers library and code-specific data

Building LLM-powered Applications

• Vector Embeddings
• Ingesting Private Data with LlamaIndex
• Types of Indexing and Chunking for Data Ingestion
• Introduction to Retrieval Augmented Generation (RAG)
• Semantic Search for Code libraries

LangChain Integration and Advanced RAG

• LLM Chains and Prompt Templates
• The LangChain “Tools” Library
• Enterprise-grade RAG Pipelines
• RAG Pipeline Optimization and Performance Monitoring

Enterprise API Applications

• Generative AI Tech Stack
• Scalable and Efficient Architectures
• Privacy/Security Considerations with Enterprise Data
• Conversational Agents in Enterprise
• Best Practices for production-ready LLM Applications
• Enterprise Application Pipelines
• Choosing the right foundation model
• Cost and ROI Evaluation Strategy

LLM Deployment for Developers

• LLM Deployment Frameworks
• Introduction to LLMOps for Developers
• LLM Security Considerations
• Enterprise Privacy
• Cloud Deployment vs Local (Private) Serving

Prerequisites

• Practical experience in Python (at least 6 months):
  • Data Structures, Functions, Control Structures
  • Exception Handling, File I/O, async, concurrency (recommended)
• Practical experience with these Python libraries: Pandas, NumPy, and scikit-learn
• Understanding of Machine Learning concepts - regression, clustering, classification
  • ML Algorithms: Gradient Descent, Linear Regression
  • Loss Functions and evaluation metrics