> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chronosphere.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Generate queries using natural language
Chronosphere Observability Platform provides
[generative artificial intelligence](/overview/generative-ai) (AI) tools to generate
[PromQL](/investigate/querying/promql) or [logs](/investigate/querying/query-logs)
queries from natural language prompts. You can generate queries when querying data
in an explorer or when defining the query for a
[monitor](/investigate/alerts/monitors) or [service level objective](/observe/slo)
(SLO).
Prompt AI with natural language that reflects what you want Observability Platform
to query, and Observability Platform generates queries that return relevant data
based on those prompts. You can converse with AI to provide more context, refine
the generated query, and ask questions that could be answered by new queries.
Observability Platform uses [semantic search](#semantic-metric-search) to identify
the most relevant metrics for your prompt, so you don't need to know exact metric
names to generate accurate queries.
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.
## Generate queries from natural language prompts
You can generate queries from natural language in
[Metrics Explorer](/investigate/querying/metrics/explorer),
[Logs Explorer](/investigate/querying/query-logs), and when defining the query for
a [monitor](/investigate/alerts/monitors#create-a-monitor) or [SLO](/observe/slo).
Fields that support natural language queries include an **Edit with AI** button.
1. Open [Metrics Explorer](/investigate/querying/metrics/explorer) to query metrics,
open [Logs Explorer](/investigate/querying/query-logs) to query logs, or open the
**Query** section of a monitor or SLO indicator to generate queries for alerting.
2. In the query field, click **Edit with AI** to open the natural language prompt
field. You can also click or focus on the query field and use the keyboard shortcut
Control+I (Command+I on macOS) to open the prompt field.
3. In the **Describe your query** prompt field that appears, write your query as
a [natural language prompt](#write-effective-natural-language-prompts).
4. Click ** Generate** or press Enter
(Return on macOS) to submit your prompt.
5. Optional: To cancel a prompt being processed, click ** Stop**.
Prompts must be related to monitoring or relevant observability data, and must
also be specific about the services, metrics, or logs being queried. If you submit
a prompt that doesn't appear to be related to monitoring or lacks specificity,
Observability Platform notifies you or requests more information.
Observability Platform populates the query field with its generated query and presents
actions that you can take with the generated query.
* To immediately run the generated query, click **Accept and run** or press Control+Enter
(Cmd+Return on macOS).
* To accept the generated query without running it, click **Accept** or press Tab.
* To revert the query field to its previous state, click **Reject** or press Esc.
You can also manually edit the generated query and
[refine it with additional prompts](#refine-your-query-with-additional-prompts).
## Refine your query with additional prompts
When you enter a prompt while editing with AI, Observability Platform generates
a query based on it. If you've generated a query, manually entered a query, or
opened a query in an explorer from elsewhere in Observability Platform, you can
also refine that query using AI.
To provide additional prompts, click the natural language prompt field, now named
**Refine your query**, and enter a new prompt.
Observability Platform adjusts the query with this new context and presents it
as a diff. The original prompt is listed first with a red indicator, followed by
the new suggestion with a green indicator.
After each iterative prompt, Observability Platform either presents the same **Accept and run**,
**Accept**, and **Reject** options, or notifies you of issues with your prompt.
## Generate queries for SLO indicators
You can generate PromQL queries from natural language when you define the indicator
for an [SLO](/observe/slo). SLO query generation uses the same **Edit with AI** workflow,
[refinement](#refine-your-query-with-additional-prompts), and
[semantic metric search](#semantic-metric-search) as Metrics Explorer.
For more information, see [Service level objectives](/observe/slo).
## Generate queries for monitors
You can generate queries from natural language when you define the query for a
[monitor](/investigate/alerts/monitors). Monitor query generation uses the same
**Edit with AI** workflow, [refinement](#refine-your-query-with-additional-prompts),
and [semantic metric search](#semantic-metric-search) as the explorers, and produces
queries that are valid for alerting.
Query generation is available for monitors that use **Prometheus** (PromQL) and
**Logs** data sources.
For more information, see [Monitors](/investigate/alerts/monitors).
## Semantic metric search
When you generate a PromQL query from a natural language prompt, Observability
Platform uses semantic search to find the metrics most relevant to your prompt.
Semantic search matches the intent of your prompt against metric names and
descriptions, rather than relying on exact keyword matches.
For example, if you submit a prompt with "show me CPU usage across my cluster," semantic
search identifies metrics like `container_cpu_usage_seconds_total` or
`node_cpu_utilization` even though your prompt doesn't mention those metric names
directly. This means you can describe what you want to observe in plain language,
and Observability Platform selects the appropriate metrics to query.
Semantic search runs automatically as part of the natural language query generation
workflow. You don't need to configure or enable it separately.
## Write effective natural language prompts
You might not have enough information to write effective prompts when you begin
using AI to generate a query. However, you can iterate by repeatedly
[refining your query](#refine-your-query-with-additional-prompts) with a goal of
providing enough context to generate a query tailored to your issue.
To write effective prompts for queries:
* Write the goal of the query you want to generate as a specific and concise statement.
* Provide relevant context that you already have, such as metric or label names.
[Semantic search](#semantic-metric-search) can find relevant metrics even without
exact names, but include them when you know them improves results.
* State the form of data you want, such as a count, rate, or histogram.
* Avoid overly terse, vague, ambiguous, or irrelevant prompts.
For example, an overly terse prompt is unlikely to provide enough context to generate
a sufficiently precise query, and might not provide enough information to generate
any query:
> downtime
A vague and imprecise prompt with unnecessary information is less likely to generate
an actionable query:
> Tell me why I'm getting so many downtime alerts this week.
While this might produce a query, you can iterate on the result to focus on more
specific issues.
A more concise and precise prompt helps the large language model generate a more
focused query:
> Which shopping cart service alerts were triggered in the last 7 days, and why?
Providing more information or refining your criteria can further focus the result:
> Show success rates versus failures for requests to the shopping cart service in
> the last 7 days.
Once you begin generating relevant queries, you can engage more conversationally
with the AI as you continue iterating. The editor retains past context as you continue
prompting to refine your query.
### Troubleshoot generated queries
Confirm that the [time range selector](/navigate/time-ranges) is set to a relevant
span of time for your prompt.
To investigate the query and identify any errors or unexpected results, click
**Debug**. You can copy and paste any reported errors as prompts that
[refine the query](#refine-your-query-with-additional-prompts).
Remember that the resulting query represents the large language model's hypothesis,
and isn't a definitive answer to your question. For example, it might not select
the most relevant telemetry data for your request. Critically review the generated
query and manually edit when necessary. Observability Platform incorporates your
edits in subsequent prompts to guide further refinements or changes to the generated
query.
## Close the natural language query field
To close the natural language query field, click **X** or press Control+I
(Command+I on macOS).