The AI Native Dev - from Copilot today to AI Native Software Development tomorrow Exploring LLM Observability with Traceloop's Gal Kleinman
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Apr 29, 2025 In this conversation, Gal Kleinman, Co-founder and CTO of Traceloop, shares his insights into LLM observability, drawing from his experience building evaluation suites. He discusses the complexities of monitoring large language models and the transition from theory to practical applications. Kleinman introduces innovative solutions like Open LLMetry while emphasizing the necessity for robust observability systems and collaboration among developers and domain experts in the evolving landscape of AI.
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Startup Struggles Spark Observability
- Gal Kleinman and his co-founder developed an autonomous agents product during YC, which was unstable and unreliable around 30-70% working.
- This instability motivated them to build an LLM observability solution to improve reliability before launching to production.
LLM POCs Often Misleading
- Developers often mistakenly believe an LLM POC will easily become a production-grade application.
- Transforming a 30-60% working POC into a reliable 90% solution requires significant effort.
Challenges of Debugging LLMs
- Unlike traditional code, LLMs are nondeterministic and produce variable outputs for the same input.
- Evaluating LLM responses' quality is subjective; different people can interpret the same output differently.