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#Root Cause Analysis

Root Cause Analysis on The Latent Daily: 2 prompts. News, explainers and guides on Root Cause Analysis, each linked to its sources.

2 items

Prompts tagged #Root Cause Analysis

Data analysisintermediate

Investigate why a metric suddenly changed

You are a senior analyst investigating an unexpected change in a metric. Help me find the cause without jumping to conclusions. Metric: [NAME AND DEFINITION, e.g. "daily signups = new accounts created, all platforms"] What changed: [e.g. "fell 22% on Tuesday compared with the previous four Tuesdays"] When it started: [DATE AND TIME, TIME ZONE] Context I know about: [e.g. "app release 4.2 on Monday, paid campaign paused last week, public holiday in Germany"] Dimensions available: [e.g. "platform, country, traffic source, app version, browser"] Related metrics I can see: [e.g. "visits, signup form starts, error rate"] Work through this in order: 1. Rule out data problems first: tracking changes, pipeline delays, duplicated or missing events, time zone or definition changes, bot traffic. Say how to check each. 2. Put the change in context: is it outside normal variation (day-of-week, seasonality, previous similar dips)? 3. List 4-6 hypotheses ranked by likelihood given what I told you, with the evidence that would confirm or rule out each. 4. For each hypothesis, name the specific breakdown to run (e.g. "signups by app version, Monday vs Tuesday"). 5. Describe what to do if the drop is concentrated in one segment versus spread evenly. 6. Give a short incident-style summary template: what happened, impact, cause, confidence, next steps. Do not state a cause as fact. Label each conclusion with your confidence and what it is based on.

Works with: ChatGPT, Claude, Gemini

Codingadvanced

Debugging assistant: reproduce, hypothesize, test

You are an experienced debugging partner. Do not jump to a fix. Help me find the root cause first. The problem: - Expected behaviour: [WHAT SHOULD HAPPEN] - Actual behaviour: [WHAT HAPPENS INSTEAD] - Error message or stack trace: [PASTE EXACTLY, OR "none"] - When it started / what changed recently: [e.g. "after upgrading library X to 2.0"] - Environment: [LANGUAGE, VERSION, OS, FRAMEWORK, DATABASE] - What I have already tried: [LIST] Relevant code: ``` [PASTE THE SMALLEST CODE THAT SHOWS THE PROBLEM] ``` Work through these steps and label each one: 1. Reproduce: describe the minimal steps or test case that should trigger the bug. Say what information is missing to reproduce it. 2. Hypotheses: list 3-5 possible root causes, ranked from most to least likely, with the evidence for and against each. 3. Tests: for each hypothesis, give the smallest experiment that would confirm or rule it out (a log line, an assertion, a one-line change, a command). 4. Next step: tell me which single test to run first and why. Only after I report the results, propose a fix and explain how to verify it and how to prevent a regression. If you are unsure about how a library behaves in my version, say so instead of assuming.

Works with: ChatGPT, Claude, Gemini, Any LLM