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Best AI Prompts Directory for Science & Engineering

AI Prompts

Simply the Biggest AI Prompts Directory Specialized in Product Design and Innovation

Ai prompts for product design
A comprehensive ai prompts directory designed to enhance product design, engineering, and innovation through optimized data processing and solution generation.

Welcome to the world’s largest AI prompts directory dedicated to advanced product design, engineering, science, innovation, quality, and manufacturing. While online AI tools are rapidly transforming the engineering landscape by augmenting human capabilities, their true power is unlocked through precise and expertly crafted instructions. This comprehensive directory provides you a collection of such prompts, enabling you to command AI systems that can process vast amounts of data, identify complex patterns, and generate novel solutions far more efficiently than traditional methods.

Discover and fine tune the exact prompts needed to leverage online AI agents for optimizing your designs for peak performance and manufacturability, accelerating complex simulations, accurately predicting material properties, and automating a diverse range of critical analytical tasks.
The advanced search filters allow fast access to this extensive directory and cover the full spectrum of modern engineering.

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  • Risk Assessment and Safety Analysis
  • Electrical engineering

AI Prompt to Quantitative Risk Analysis for Electrical Systems

  • Electrical Engineering, Failure analysis, Failure Mode and Effects Analysis (FMEA), Predictive Maintenance Algorithms, Process Improvement, Quality Management, Risk Analysis, Risk Management, Safety

This prompt asks the AI to perform a quantitative risk analysis on a specified electrical system, using input data like failure rates and exposure times. The user inputs failure data and system parameters.

Output: 

  • CSV
  • does not require live Internet
  • Fields: {failure_rates_data} {system_parameters} 
				
					Using the following failure rates data in CSV format: 
 {failure_rates_data} 
 and system parameters: 
 {system_parameters} 
 calculate quantitative risk metrics such as Failure Probability, Risk Priority Number (RPN), and expected downtime. Return a CSV table with columns: Component, FailureRate, Severity, Occurrence, Detection, RPN, MitigationActions. Explain calculations briefly in comments if possible.
  • Best for: Best for data-driven risk quantification and prioritization
  • Risk Assessment and Safety Analysis
  • Electrical engineering

AI Prompt to Evaluate Safety Measures for Electrical Design

  • Design Evaluation, Design for Sustainability, Electrical Conductance, Electrical Engineering, Electrical Resistance, Risk Management, Safety, Standards

This prompt directs the AI to evaluate the effectiveness of specified safety measures in an electrical design based on provided design details and standards. The user inputs design features and relevant safety standards.

Output: 

  • Markdown
  • does not require live Internet
  • Fields: {design_features} {safety_standards} 
				
					Given the electrical design features: 
 {design_features} 
 and the following safety standards: 
 {safety_standards} 
 evaluate the adequacy of the implemented safety measures. Provide a detailed markdown report with sections for compliance, potential weaknesses, and recommendations for improvement. Use bullet points and bold important terms.
  • Best for: Best for compliance checking and improving design safety
  • Risk Assessment and Safety Analysis
  • Electrical engineering

AI Prompt to Electrical System Risk Identification

  • Electrical Conductance, Electrical Engineering, Electrical Resistance, Environmental Impact Assessment, Failure Mode and Effects Analysis (FMEA), Hazard and Operability Study (HAZOP), Risk Analysis, Risk Management, Safety

This prompt helps identify potential risks and failure modes in a specified electrical system or component. The user inputs the system description and operating conditions, and the AI outputs a structured risk list with severity and likelihood assessments.

Output: 

  • JSON
  • does not require live Internet
  • Fields: {electrical_system_description} {operating_conditions} 
				
					Based on the following electrical system description: 
 {electrical_system_description} 
 and the operating conditions: 
 {operating_conditions} 
 identify all potential risks, failure modes, and hazards. For each risk, provide an assessment of severity (High, Medium, Low) and likelihood (High, Medium, Low). Format the output as a JSON array with objects containing RiskDescription, Severity, Likelihood, and SuggestedMitigation.
  • Best for: Best for early-stage hazard identification and risk planning
  • Literature Review and Trend Analysis
  • Electrical engineering

AI Prompt to Analyze Evolution of Electrical Engineering Technologies

  • Electrical Conductance, Electrical Engineering, Electronics, Engineering, Innovation, Product Development, Product Lifecycle, Renewable Energy, Sustainability Practices

This prompt asks the AI to analyze the historical evolution and future outlook of a specific electrical engineering technology or concept. The user provides the technology name and timeline.

Output: 

  • Markdown
  • does require live Internet
  • Fields: {technology_name} {timeline} 
				
					Analyze the historical development and evolution of the following electrical engineering technology: 
 {technology_name} 
 over this timeline: 
 {timeline} 
 Provide a markdown formatted report including key milestones, technological advances, influential researchers, and predicted future trends. Use headings, bullet points, and timeline tables where appropriate.
  • Best for: Best for understanding technology lifecycle and forecasting
  • Literature Review and Trend Analysis
  • Electrical engineering

AI Prompt to Generate Bibliography of Seminal Papers

  • Artificial Intelligence (AI), Cybersecurity, Electrical Engineering, Machine Learning, Neural Network, Robotics, Software Engineering, Systems Modeling Language (SysML)

This prompt instructs the AI to generate a bibliography of seminal papers in a specified electrical engineering subfield. The user inputs the subfield and optionally filters such as date or authors.

Output: 

  • CSV
  • does require live Internet
  • Fields: {electrical_subfield} {filters} 
				
					Generate a CSV bibliography list of seminal papers in the electrical engineering subfield: 
 {electrical_subfield} 
 applying these filters if any: 
 {filters} 
 The CSV must include columns: PaperTitle, Authors, Year, JournalOrConference, DOI or URL. Sort by relevance and citation count if possible.
  • Best for: Best for compiling authoritative reference lists for literature reviews
  • Literature Review and Trend Analysis
  • Electrical engineering

AI Prompt to Identify Knowledge Gaps in Electrical Engineering Literature

  • Electrical Conductance, Electrical Engineering, Electrical Resistance, Electronics, Engineering, Environmental Engineering, Renewable Energy, Sensors, Signal Processing

This prompt helps identify knowledge gaps in scholarly electrical engineering literature on a given topic. The user inputs the topic and optionally key papers or keywords.

Output: 

  • Text
  • does require live Internet
  • Fields: {topic} {key_papers_or_keywords} 
				
					For the electrical engineering topic: 
 {topic} 
 and considering the following key papers or keywords: 
 {key_papers_or_keywords} 
 analyze existing literature to identify knowledge gaps, underexplored areas, and opportunities for future research. Provide a structured text report with sections for each gap identified and supporting rationale.
  • Best for: Best for guiding research planning and proposal writing
  • Literature Review and Trend Analysis
  • Electrical engineering

AI Prompt to Summarize Latest Electrical Engineering Research Trends

  • Advanced Driver-Assistance Systems (ADAS), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), Electrical Engineering, Machine Learning, Renewable Energy, Research and Development, Sustainability Practices

This prompt guides the AI to summarize the latest research trends in a specified electrical engineering topic using current academic databases or its knowledge base. The user inputs the research topic and optionally a date range.

Output: 

  • Markdown
  • does require live Internet
  • Fields: {research_topic} {date_range} 
				
					Using the research topic: 
 {research_topic} 
 and the date range: 
 {date_range} 
 please summarize the latest research trends in electrical engineering. Include key breakthroughs, emerging technologies, and dominant research themes. Format the summary in markdown with headings, bullet points, and references to seminal papers if possible.
  • Best for: Best for staying updated on cutting-edge research areas
  • Translation and Language Adaptation
  • Electrical engineering

AI Prompt to Adapt Electrical Engineering Report for International Audience

  • Design for Sustainability, Electrical Engineering, Environmental Impact Assessment, Global Positioning System (GPS), Project Management, Quality Management System (QMS), Sustainable Development, User-Centered Design

This prompt enables the AI to adapt a technical electrical engineering report to suit an international audience by adjusting units, terminology, and style. The user inputs the original report text and target region.

Output: 

  • Text
  • does require live Internet
  • Fields: {original_report_text} {target_region} 
				
					Adapt the following electrical engineering technical report text: 
 {original_report_text} 
 to suit an international audience from the target region: 
 {target_region} 
 Convert all units to the preferred system, adjust terminology and spellings, and simplify complex sentences while preserving technical accuracy. Provide the adapted text as a continuous paragraph with clear formatting.
  • Best for: Best for preparing technical documents for global distribution
  • Translation and Language Adaptation
  • Electrical engineering

AI Prompt to Simplify Electrical Jargon for Non-Engineers

  • Design for Manufacturing (DfM), Design Thinking, Electrical Conductance, Electrical Engineering, Electrical Resistance, Electronics, Engineering, Quality Assurance, Quality Control

This prompt instructs the AI to convert a list of electrical engineering technical terms and phrases into simple explanations understandable by non-engineers. The user provides the list of terms.

Output: 

  • JSON
  • does not require live Internet
  • Fields: {technical_terms_list} 
				
					Given the following list of electrical engineering technical terms: 
 {technical_terms_list} 
 provide a JSON object where each term is a key and the value is a simple, clear explanation suitable for a non-engineer audience. Keep explanations concise and avoid technical jargon. Capitalize terms in keys.
  • Best for: Best for creating glossaries or training materials for mixed audiences
  • Translation and Language Adaptation
  • Electrical engineering

AI Prompt to Convert Electrical Engineering Paper from English to German

  • Design for Additive Manufacturing (DfAM), Design for Manufacturing (DfM), Electrical Conductance, Electrical Engineering, Electrical Resistance, Electronics, Engineering, Quality Assurance, Quality Management

This prompt asks the AI to translate a technical electrical engineering research paper excerpt from English to German, preserving all technical meanings and terminology. The user provides the excerpt text.

Output: 

  • Text
  • does require live Internet
  • Fields: {english_text_excerpt} 
				
					Translate the following electrical engineering research paper excerpt from English to German, ensuring all technical terms and jargon are accurately preserved: 
 {english_text_excerpt} 
 Provide the translated text in clear, formal German suitable for academic or professional use.
  • Best for: Best for bilingual professionals needing precise technical translations
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    Topics covered: test prompts, validation, user input, data collection, feedback mechanism, interactive testing, survey design, usability testing, software evaluation, experimental design, performance assessment, questionnaire, ISO 9241, ISO 25010, ISO 20282, ISO 13407, and ISO 26362..

    1. Lexi Peña

      No one discussing the potential bias in AI selection for these directories? AI isnt immune to prejudices, folks.

      Reply

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