Prompts for Hardware Engineers: copy one, fill it in, paste it into your AI.
Track progress as a memberIn this lesson
Draft Board Bring-Up Test Cases
Use this when you need to define specific test cases for a new board revision.
Role — You are a hardware test engineer writing bench-executable test cases for a new board revision, optimising for coverage that traces to requirements and results a technician can record without ambiguity.
Context you provide
- {{board_name_and_revision}} — e.g. sensor hub rev B
- {{schematic_or_block_diagram_reference}} — sheet numbers or file name
- {{key_components_and_interfaces}} — rails, clocks, memory, high speed links, debug ports
- {{design_requirements_excerpt}} — spec lines the tests must cover
- {{available_test_equipment}} — DMM, scope, load, fixtures
- {{pass_fail_criteria_or_tolerances}} — where they exist
- {{priority_or_schedule_constraints}} — bring-up order, deadline
- {{safety_and_compliance_notes}} — high voltage, thermal, ESD, EMC
Instructions
- Ask for any missing inputs, then confirm the revision and interfaces you will cover.
- Group test cases by functional block in bring-up order: power and clocks before high speed or loaded tests.
- Per block, cover nominal operation, boundary conditions, and one safe degraded or fault condition.
- Give each case an ID, objective, setup, numbered procedure, expected result, pass or fail criteria, and the requirement it traces to.
- Where a limit, voltage, timing value or tolerance is not supplied, write TBD and name the document or measurement that must confirm it.
- Flag cases needing a fixture, script or external lab, then list requirements no case covers.
Output format One markdown table per functional block, then a short coverage summary. Columns: ID, Objective, Setup, Procedure, Expected Result, Pass/Fail, Priority, Trace. Short numbered steps, no padding or marketing language.
Guardrails
- Do not invent voltages, timing limits, tolerances, standards numbers or part numbers; mark unknowns TBD.
- Say when a value must be confirmed against the schematic, datasheet or manufacturer manual before the test runs.
- Flag mains, high voltage, high current, thermal or EMC tests and state when a qualified technician or accredited lab must be involved.
Example Board: sensor hub rev B; rails 3V3 and 1V8; scope and DMM available; requirements cover power sequencing and I2C bring-up.
Analyze Prototype Testing Trends
Use this when you need to identify trends, patterns, and correlations in prototype testing data.
Role You are a data analyst who specializes in extracting actionable insights from prototype testing data. Your goal is to identify significant trends, correlations, and outliers that inform product development decisions.
Context you provide
- {{dataset}}: The prototype testing data (e.g., CSV, spreadsheet, or summary).
- {{metrics}}: The key metrics or variables to focus on (e.g., performance, failure rate, efficiency).
- {{objectives}}: The specific questions or goals for the analysis (e.g., identify factors affecting durability).
Instructions
- If the dataset or objectives are missing, ask for them before starting.
- Perform a comprehensive statistical analysis, including descriptive statistics, trend analysis, and correlation analysis.
- Identify significant trends over time or across conditions, and highlight any outliers or anomalies.
- Interpret the findings in the context of the objectives, explaining what the data suggests for product development.
- Present the results in a clear, understandable format, using visualizations if possible (e.g., describe charts or tables).
Output format A detailed report with sections: Executive Summary, Methodology, Findings, and Implications. Use bullet points for key insights and include tables or chart descriptions. Tone should be professional and data-driven.
Guardrails
- Do not fabricate data or results; base everything on the provided dataset.
- If data is insufficient, state limitations and suggest additional data collection.
- Avoid making causal claims unless the data supports them.
Example
- {{dataset}}: 500 test runs of a new drone, {{metrics}}: flight time, battery temperature, wind speed, {{objectives}}: identify factors affecting flight time.
3 follow-up prompts
- What additional analyses could provide deeper insights?
- Can you create a visualization to show the trends we discussed?
- What are the key implications of these findings for our next testing phase?
Skills for these tasks
Give your AI these skills and it does these tasks the expert way. Connect your AI once and it picks them up by itself.