Skip to main content

​
Example Digest Prompts

The following are example digest prompts that can be used for reporting. Please modify them to work with your specific services, pipelines, and monitoring setup.

​
Daily Service Anomaly Report

Problem Statement: Proactively detect emerging issues in production services before they escalate into customer-impacting incidents. This digest compares recent error and warning patterns against baseline behavior to surface new anomalies. Prompt: 
Execute the following runbook for service 'api-server':
1. Analyze both errors and warnings for the last 24 hours, compare that with the previous 3 days (i.e. from 4 days ago to 1 day ago), do you see any new issues emerging?
2. For any new issue identified (pick the top 5 if many), find the potential root cause by
  a. Querying sample traces if available
  b. Examining any recent code or config changes made to the relevant files
3. Generate a report by deep diving into each new issue. The report should cover:
  a. Severity
  b. Number of occurrences
  c. Example error message or trace
  d. Impact analysis
  e. Potential root cause
  f. Related PRs if any
  g. Recommended next steps

​
Weekly CI Pipeline Health Report

Problem Statement: Track CI/CD pipeline reliability and identify flaky tests or infrastructure issues that slow down development velocity. This digest analyzes failed builds to help teams prioritize reliability improvements. Prompt: 
For the 'production-release' pipeline over the most recent whole week Monday 00:00:00 PDT to Sunday 23:59:59 PDT
- Find all failed builds on the 'main' branch, record the build numbers
- For each failed build identified, query the failed jobs and their logs to analyze the failure reason
- Summarize your findings in the following report

Report: Top CI Failures

Generate a table in markdown in the following format:

CI Job Name | Failure Count (# of Builds) | Example Link | List of Build Numbers
... | ... | ... | ...
... | ... | ... | ...

List the top 10 frequently failed jobs in the table.

​
Log Volume Analysis for Cost Optimization

Problem Statement: Identify opportunities to reduce observability costs by analyzing log volume patterns. This digest surfaces specific services and log messages generating excessive volume that could be sampled, filtered, or optimized. Prompt: 
I'm conducting a logs analysis to see what is contributing the most to log volume over the last 2 days. Follow the instructions and generate a report for me.

1. Determine the top 10 services for logs by volume over the past 2 days.
- Use logs aggregation query faceting on 'service'.
- Use the timeframe for the last 2 full days, e.g. day 1 00:00:00 UTC to day 2 23:59:59 UTC.

2. For each of the top 10 services, determine the top 10 'message' by log volume over the past 2 days.
- Use logs aggregation query filtering on 'service' and faceting on 'message'.
- Use the timeframe for the last 2 full days, e.g. day 1 00:00:00 UTC to day 2 23:59:59 UTC.
- Make one call per service.
- Some log source types may not allow you to query based on 'message' facet. Do not include these in the report. Rather, add it as a subnote that it cannot be queried for further analysis.

Generate a report explaining what is contributing the most to log volume. In particular, format the final report as follows (including a table)

### Indexed Log Volume Analysis

#### Recommendations

[2-3 bullet points with recommendations for reducing indexed log volume]

#### Top 20 Log Messages by Indexed Log Volume Count

| Service | Message | Daily Indexed Log Volume Count |
| ... | ... | ... |
| ... | ... | ... |

(Table should be sorted by log volume count and include the top 20 messages with highest log volume count. Divide the 2-day log volume count by 2 to get the daily log volume count.)