Construct and manage Large Language Model (LLM) workflows using Python. This course covers essential libraries like LangChain and LlamaIndex, API interactions, prompt engineering, Retrieval-Augmented Generation (RAG), testing strategies, and deployment practices. Gain practical skills to build applications powered by LLMs.
Prerequisites Intermediate Python skills
Level:
Environment Setup
Configure a Python development environment suitable for LLM workflow development.
API Integration
Interact effectively with various LLM provider APIs using Python.
Workflow Orchestration
Utilize LangChain to build and manage complex LLM chains and agents.
Data Handling
Employ LlamaIndex for efficient data loading, indexing, and querying in LLM applications.
RAG Implementation
Construct Retrieval-Augmented Generation systems to ground LLM responses in specific data.
Prompt Engineering
Apply effective prompt engineering techniques directly within Python code.
Testing & Evaluation
Implement testing and evaluation strategies specific to LLM-based systems.
Deployment
Understand best practices for packaging, deploying, and monitoring Python LLM applications.
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