Unlocking the Power of LLM Monitoring & Observability

Table of Contents

1. Introduction
2. OpenAI and Llama Index
3. Frameworks in Technical Talks
4. Observability and LM Troubleshooting
5. The Importance of LM Traces and Spans
6. An Overview of Phoenix and Rise
7. The Role of Observability in Chat-to-Purchase Experiences
8. Troubleshooting LM Calls and Prompts
9. The Challenges of Distributed Systems
10. Understanding and Improving Traces and Spans
11. Introducing Evals: Evaluating and Improving LM Performance
12. Phoenix for Development: Open Source Solutions
13. Rise for Production: Enterprise Monitoring Platform
14. The Benefits of Rigorous Evaluations using Phoenix's Library
15. Conclusion

Introduction

In this technical talk, the speaker addresses a tech-savvy audience and engages in a discussion about the usage of OpenAI and Llama Index. The speaker emphasizes the importance of frameworks in the field and discusses the role of observability in troubleshooting and analyzing systems. They highlight the unique features of Phoenix, an open-source solution, and Rise, an enterprise platform for observability. The speaker focuses on the challenges faced in the LM (Language Model) domain and explains the significance of LM traces and spans. They introduce the concept of evals (evaluations) and how they can enhance LM performance. The talk concludes with a demonstration of Phoenix's features and the upcoming launch of the evals library.

OpenAI and Llama Index

The speaker begins by querying the audience about their familiarity with OpenAI and Llama Index. They acknowledge the high usage and adoption of these frameworks among the audience, indicating a tech-savvy environment. This sets the tone for a technical discussion that will follow. The speaker also mentions other frameworks like Erling Chain, highlighting the technical expertise of the audience.

Frameworks in Technical Talks

The speaker acknowledges the importance of frameworks in technical discussions and mentions that the audience consists of technical individuals, ensuring that the talk will Delve into technical details. They encourage the audience to ask questions and engage in an interactive session. The speaker Hints at the complexity of their upcoming discussion, giving a heads-up to the audience.

Observability and LM Troubleshooting

The speaker introduces the concept of observability and explains its relevance in troubleshooting, debugging, analyzing, and evaluating systems. They highlight the pain point associated with understanding and troubleshooting LMs, emphasizing the challenges faced by those using frameworks like Lang Chain, Llama Index, and Open AI. This establishes a common problem faced by many in the audience, making the topic relatable.

The Importance of LM Traces and Spans

In the Context of observability, the speaker introduces LM traces and spans, which are similar to the concepts used in Llama Index and Lang Chain. They explain how these techniques help in comprehending the complexity of LM behavior and aid in troubleshooting. The speaker mentions the various aspects of LM observability, such as evaluating prompts and fine-tuning models, which will be covered in Detail later in the talk.

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