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This feature isn’t available to all Chronosphere Observability Platform users and might not be visible in your app. For information about enabling this feature in your environment, contact Chronosphere Support.
Chronosphere Observability Platform provides content generated by artificial intelligence (AI) tools, including large language models (LLMs). This content can serve many purposes, such as context-aware query language completions and high-level summaries of your data and visualizations.
Generative AI features can produce incorrect results, hallucinate data, and deliver inaccurate analysis. Use generative AI features with care, and independently verify all information produced by generative AI tools before applying it.Certain prompts, data, or other inputs might produce irrelevant content. Don’t rely on generative AI features or responses for any uses that exceed their designed scope.

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Discover generative AI features

Generative AI features aren’t enabled on all Chronosphere tenants. For more information, contact Chronosphere Support.

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Identify generative AI features

Generative AI features are accompanied by icons in the Observability Platform interface:
  • AI Summary: Produces a pop-up summary of an entity or subject. You can provide feedback for these summaries by clicking their accept or reject buttons.
  • AI content: Appears alongside content created by generative AI tools.

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Generative AI features in Observability Platform

Observability Platform features that use generative AI include:
  • Dashboard descriptions summarize dashboards by their contents, panel groups, and variables.
  • Dashboard panel names and descriptions create a name and summary description for a panel based on panel contents.
This feature is in Early Access (EA), and might not be visible in your app. To learn more about this program and the features it contains, see the Early access page.

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Write effective prompts

The quality of a generative AI response depends on the prompt that produced it. The guidance in this section applies to any prompt field in Observability Platform, including the Assistant and query generation.

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Prompting guidelines

  • State a specific goal. Name the service, metric, log field, or resource you care about, and what you want to know about it.
  • Bound the question in time. Generative AI features default to the time range of the page you’re on, which might not match your intent. State the window when it matters.
  • Provide the context you have. Metric names, label values, service names, and dashboard slugs all narrow the search. You don’t need exact names, because Observability Platform uses semantic search to find relevant metrics, but exact names produce better results faster.
  • Ask for the shape of the answer you want, such as a count, a rate, a comparison against last week, or a table of the top contributors.
  • Ask one question at a time. A prompt that combines several unrelated requests produces a diffuse answer. Send follow-up prompts instead, because both the Assistant and query generation retain the context of the conversation.
  • Refine instead of restarting. When the first response is close but not correct, describe the correction rather than rewriting your original prompt.
  • Verify before you act. Read the queries and evidence behind a response, and confirm them against your data before making a decision.

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Example prompts

An overly terse prompt lacks the context to produce an actionable answer:
latency
A prompt that’s vague and adds no constraints produces a diffuse answer:
Tell me what’s wrong with my services right now.
A specific prompt that names the subject, the time window, and the form of the answer produces a focused result:
Show the p99 latency of the checkout service over the last 6 hours, broken down by endpoint.
Effective prompts for the Assistant follow the same pattern across use cases:
  • Explain a signal:
    Why did the checkout-api error rate increase around 14:00 UTC today?
  • Find a resource:
    Which dashboards cover the payments service, and who owns them?
  • Understand configuration:
    What conditions trigger the checkout-latency monitor, and who does it notify?
  • Correlate across telemetry types:
    Were there any deploys or config changes to the payments service in the 30 minutes before this alert triggered?
  • Investigate logs:
    What are the most common error messages in the shopping-cart service logs over the last hour, and how often does each occur?
  • Change a dashboard:
    Add a panel to this dashboard showing request rate by status code for the checkout service.
When a response is close but incomplete, refine it with a follow-up rather than a new prompt:
Break that down by availability zone instead, and exclude the canary deployment.