How OpenTelemetry Helps Generative AI - Phillip Carter, Honeycomb

Summary

Philip from Honeycomb's product team discusses the role of Open Telemetry in improving generative AI applications. He emphasizes the importance of observability to understand user interactions and model performance, highlighting challenges in managing costs and ensuring reliability with AI's unpredictable nature. The talk explores the practical aspects of building AI applications, the use of language models, and the significance of context and prompting techniques. It also touches on the ongoing work within the Open Telemetry community to standardize tracing and metrics for AI applications.

Takeaways

• 😀 Open Telemetry is a crucial tool for improving generative AI applications, despite being considered one of the least interesting parts of the project by the speaker.
• 🔍 The speaker emphasizes the importance of observability in AI, especially in understanding user inputs and outputs to make AI applications more reliable.
• 💡 AI and generative models are becoming more accessible and affordable, shifting the bottleneck from large tech companies to the broader developer community.
• 🚀 The speaker discusses the challenges of managing costs and understanding model performance when building AI applications.
• 🔑 The key to building successful AI applications is understanding the right prompting techniques and knowing when to fine-tune or train your own language models.
• 📈 The speaker highlights the significance of having good data to feed into the AI models, as well as the right context for user inputs to produce accurate outputs.
• 🛠️ The process of building AI applications involves a stack of operations before calling a language model, including search services and retrieval-augmented generation.
• 🔬 Observability is akin to a tracing problem, where capturing the entire flow from user input to model output is essential for analysis and improvement.
• 📊 Metrics like latency, error rates, and cost are important but often easier to manage compared to ensuring the right data is fed into the model and the model behaves correctly.
• 📝 The speaker suggests logging extensive information about the AI application process, including prompts, model responses, and post-processing steps for better debugging and improvement.
• 🌐 Open Telemetry is being improved with semantic conventions for AI applications, aiming to standardize the representation of operations and data within traces and logs.

Q & A

What is the speaker's name and what team does he work for at Honeycomb?

The speaker's name is Philip, and he works for the product team at Honeycomb.

What is the main topic of Philip's talk?

The main topic of Philip's talk is how open telemetry helps generative AI, although he mentions that he won't be discussing open telemetry in depth.

What does Philip consider to be the least interesting part of the project?

Philip considers open telemetry to be the least interesting part of the project, as it should just work and be helpful without being the main focus.

What is the purpose of good observability in the context of generative AI applications?

Good observability is important for understanding what users are inputting, what the outputs look like, and how to improve the AI based on real-world usage.

What is the current state of AI in terms of accessibility and cost?

AI, particularly powerful machine learning models, is becoming more accessible and affordable for a broader audience, with costs decreasing over time.

What challenges do developers face when managing generative AI applications?

Developers face challenges such as managing costs, understanding model performance, and determining the right kind of application to build.

What is the significance of the 'killer apps' mentioned in the script?

The 'killer apps' like chat GBT and G Co-pilot represent successful applications of AI, but they also indicate the competitive landscape and the need for innovation beyond these applications.

What does Philip mean by 'inscrutable black boxes' in the context of generative AI?

By 'inscrutable black boxes,' Philip refers to the non-deterministic nature of AI models, which can be difficult to understand and predict in terms of their outputs.

What is the importance of understanding user behavior and inputs in AI application development?

Understanding user behavior and inputs is crucial for improving AI applications, as it helps developers to refine prompts, model usage, and overall application performance.

What role does open telemetry play in addressing the challenges faced by developers in AI applications?

Open telemetry plays a role in providing observability into the AI application's performance, helping developers to trace and understand the flow of data and the impact of various components.

What is the 'golden triplet' that Philip mentions for analyzing AI applications?

The 'golden triplet' refers to the combination of inputs, errors, and responses for each user request, which is essential for evaluating and improving AI application performance.