WA3509
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
Duration
2 days
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
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
