Intermediate Generative AI for Developers Training

Our Intermediate Generative AI (Gen AI) training teaches developers advanced techniques like fine-tuning LLMs, Retrieval Augmented Generation (RAG), and Vector Embeddings. Attendees also learn how to integrate LLMs into development pipelines.

Course Details


2 days


  • 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

Skills Gained

  • Develop effective prompts to accelerate software engineering through code generation workflows and pair programming best practices
  • Implement advanced techniques such as fine-tuning, RAG, and Vector Embeddings to enhance application functionality in enterprise contexts
  • Apply best practices for secure, efficient, and maintainable LLM integration in software development pipelines
Course Outline
  • 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