Beyond the Prompt: Building Robust LLM Observability with OpenLit and OpenTelemetry

LLMs are game-changing in today’s world but understanding their behavior is much more crucial as it can improve the output a thousand times. Simply observing the input and output – the "prompt" and the "response" is no longer sufficient for building robust and dependable LLM-powered applications.

LLM Observability refers to a more in-depth approach to monitoring large language models, where it captures not only the basic outputs but also metrics, traces, and patterns of behavior.Without observability, identifying and fixing anomalies, performance issues, or inaccuracies becomes difficult.

In this talk, we will talk about how we can develop a complete end-to-end Observability for LLM using OpenLit
Outcomes that LLM observability gives to enhance the performance
Response latency: How quickly the model responds to user queries.
Token usage: Tracking token consumption to manage operational costs.
Prompt effectiveness: Evaluating how well the crafted prompts generate the desired outputs.