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Intermediate Generative AI for Developers

Accelebrate's Intermediate Generative AI (Gen AI) training teaches developers advanced techniques like fine-tuning LLMs, Retrieval Augmented Generation (RAG), and Vector Embeddings. Attendees also...

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Course Code WA3509
Duration 2 days
Available Formats Classroom

Accelebrate's 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.

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

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

Course Details

Outline

Introduction

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

Conclusion