google llm observability tracing evals

If you’re looking for Google’s LLM observability, tracing, and evals tooling, the main pieces are:

1) Vertex AI / Gemini observability

Google’s core stack is Vertex AI:

• Vertex AI Gemini API / Model Garden for model access
• Vertex AI Studio for prompt testing
• Vertex AI Experiments for comparing runs
• Cloud Logging / Cloud Monitoring for operational telemetry
• Cloud Trace / OpenTelemetry for request tracing

2) Tracing

For tracing LLM apps on Google Cloud, the common pattern is:

• instrument your app with OpenTelemetry
• export traces to Cloud Trace
• attach metadata like:
  • prompt
  • model name/version
  • tokens in/out
  • latency
  • retrieval context
  • user/session/request IDs
  • evaluation scores

If you’re using frameworks like LangChain or LlamaIndex, you can often add OpenTelemetry or callback hooks to capture spans around:

• retrieval
• reranking
• prompt construction
• model calls
• post-processing

3) Evaluation

For evals, Google’s options include:

• Vertex AI Eval / model evaluation workflows for comparing outputs
• Gemini-based judging in custom evaluation pipelines
• Vertex AI Experiments to track prompt/model variants
• BigQuery for storing datasets, predictions, and eval results
• Cloud Storage for large offline eval sets

Typical eval dimensions:

• groundedness / faithfulness
• answer correctness
• toxicity / safety
• hallucination rate
• latency
• cost per request
• retrieval quality

4) Production observability stack

A common production setup on Google Cloud is:

• App / API server
• OpenTelemetry SDK
• Cloud Trace for spans
• Cloud Logging for structured logs
• BigQuery for analytics and eval datasets
• Vertex AI for model calls and experimentation
• Looker / Grafana for dashboards

5) Practical architecture

A simple architecture:

1. User request enters your service
2. Create a trace/span for the request
3. Log prompt + retrieved docs + model response metadata
4. Call Gemini or another model
5. Save traces/logs to Google Cloud
6. Run offline evals in batch and store scores in BigQuery
7. Compare prompt/model versions in Vertex AI Experiments

6) If you want a managed “LLM observability” product

Google doesn’t have one single standalone product exactly like some third-party LLM observability vendors, but the closest Google-native stack is:

• Vertex AI
• Cloud Logging
• Cloud Trace
• OpenTelemetry
• BigQuery
• Vertex AI Experiments

7) Example metrics to track

For LLM observability, I’d track:

• request latency
• model latency
• prompt size / completion size
• token usage
• retrieval hit rate
• citation coverage
• answer score from judge model
• human feedback
• error rate / retries
• cost per successful answer

If you want, I can also give you:

1. a Google Cloud reference architecture for LLM observability,
2. a Vertex AI + OpenTelemetry example, or
3. a comparison with LangSmith / Arize / Helicone / WhyLabs.