About This Program
A hands-on enterprise program designed to equip engineers with the skills needed to build, deploy, and manage AI Copilots in real business environments. Master LLM integration, context retrieval, and enterprise deployment strategies.
What You'll Learn
- Building custom Copilot interfaces with OpenAI and open-source LLMs.
- Retrieval-Augmented Generation (RAG) using vector databases.
- Tools like LangChain and LlamaIndex for orchestration.
- Security, privacy, and compliance for enterprise AI.
- Prompt engineering and fine-tuning strategies.
- Streaming responses and building user-friendly conversational UIs.
Curriculum (6 weeks)
Week 1
Foundations of Generative AI
Understanding LLMs, Transformer architecture, and the difference between training, fine-tuning, and RAG.
Week 2
Building Context-Aware Agents
Introduction to RAG pipelines, vector databases (Pinecone/Weaviate), and embedding models for enterprise data retrieval.
Week 3
Advanced Integration Strategies
Deep dive into LangChain and LlamaIndex. Chaining tools, managing conversation history, and handling complex multi-step queries.
Week 4
Enterprise AI Security
Implementing guardrails, PII redaction, role-based access controls, and secure deployment models (on-prem vs cloud).
Week 5
Optimizing for Production
Caching strategies, cost management with token usage, prompt caching, and monitoring LLM response quality.
Week 6
Capstone Project
Build an AI Copilot for a specific enterprise use case (e.g., internal knowledge base assistant or support agent) with a UI and backend integration.
Prerequisites
- Proficiency in Python or Node.js.
- Experience with APIs and databases.
- Basic understanding of machine learning concepts.
Career Outcomes
- AI Solutions Architect
- Prompt Engineer
- LLM Application Developer
- Enterprise AI Consultant
