Summary

In this podcast, Kirk Byers and John Capobianco discuss the  impact of AI on network automation and engineering. They explore the significance of ChatGPT, the challenges of inference, and the concept of Retrieval-Augmented Generation (RAG). John shares insights on using LangChain for building AI applications, and the role of AI agents. The conversation emphasizes the importance of adapting to AI technologies and the potential for enhancing productivity in network engineering.

Takeaways

• ChatGPT marked a significant turning point in AI awareness.
• Retrieval-Augmented Generation (RAG) enhances AI capabilities.
• LangChain simplifies the integration of AI with network tools.
• AI agents can automate complex tasks in network management.
• Fine-tuning models can improve AI performance in specific domains.
• AI can significantly reduce the time needed for project development.

Chapters

00:00 - Introduction to AI and Network Automation

01:42 - The Impact of ChatGPT

05:50 - Understanding Hallucinations and Inference

09:53 - Retrieval-Augmented Generation (RAG) Explained

14:42 - Building with LangChain

18:37 - Exploring Models and Local LLMs

22:55 - Exploring Fine-Tuning and RAG Techniques

25:34 - Integrating AI with Network Data

29:34 - The Rise of AI Agents

34:28 - Modernizing Code

39:53 - Future Directions for Network Engineers

Reference Materials
Selector https://www.selector.ai/
John Capobianco YouTube Video on "Multi Agent AI for Network Automation" https://www.youtube.com/watch?v=8GwSIRGae10
LangChain https://www.langchain.com/
LlamaIndex https://www.llamaindex.ai/
Streamlit https://streamlit.io/