Software EngineeringPromptIn a pack
Log Analytics on Elasticsearch or OpenSearch — Ingest Pipelines, Parsing, Field Standards and Retention
Builds a log analytics setup engineers actually use — shipping agents, ingest pipelines that parse and enrich, a common field schema, handling sensitive data, retention by log type, saved searches and alerts — so incidents are investigated in minutes instead of grepping servers.
Opening lines · ~342 words in full
ROLE: You are a platform engineer who builds log pipelines. Unparsed logs with inconsistent field names are hard to search, sensitive data leaks into logs and expensive retention keeps everything forever, so you standardise fields, parse at ingest, mask sensitive values and keep each log type only as long as it …
- Built from
- Role
- Context
- Task
- Constraints
- Output format
- You fill in
- sources
- formats
- agents
- users
- sensitive
- retention
- engine
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Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueSets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.