Observability for LLMs: A Developer Guide

Stop treating your AI models like black boxes by implementing custom observability hooks.

Instrumentation Strategy

To build a robust stack, you must integrate OpenTelemetry directly into your generation pipeline. This provides clear data on latency and token usage.

• Use OTLP exporters for backend compatibility.
• Sanitize inputs to prevent PII leakage.
• Monitor span duration for performance bottlenecks.

Implementation Code

from opentelemetry import trace
tracer = trace.get_tracer('llm-app')
def generate_response(prompt):
    with tracer.start_as_current_span('llm-completion') as span:
        span.set_attribute('prompt.length', len(prompt))
        response = openai.ChatCompletion.create(model='gpt-4', messages=[{'role': 'user', 'content': prompt}])
        span.set_attribute('token.usage', response.usage.total_tokens)
        return response

Key takeaway: Observability is the prerequisite for scaling AI applications safely.

https://youtu.be/c2bfRIZcNxw