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Best AI Prompts for Electrical Engineering

AI Prompts Electrical Engineering
Ai prompts for electrical engineering
Ai-driven tools are revolutionizing electrical engineering by enhancing design efficiency, simulation accuracy, and predictive maintenance through advanced data analysis and generative design techniques.

Online AI tools are rapidly transforming electrical engineering by augmenting human capabilities in circuit design, system analysis, electronics manufacturing, and power system maintenance. These AI systems can process vast amounts of simulation data, sensor readings, and network traffic, identify complex anomalies or performance bottlenecks, and generate novel circuit topologies or control algorithms much faster than traditional methods. For instance, AI can assist you in optimizing PCB layouts for signal integrity and manufacturability, accelerate complex electromagnetic or power flow simulations, predict semiconductor device characteristics, and automate a wide range of signal processing and data analysis tasks.

The prompts provided below will, for example, help with generative design of antennas or filters, accelerate simulations (SPICE, EM field simulations, power system stability analysis), help with predictive maintenance where AI analyzes sensor data from power transformers or grid components to forecast potential failures, enabling proactive servicing and minimizing downtime, help with semiconductor material selection or optimal component selection (e.g., choosing the best op-amp for specific parameters), and much more.

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AI Prompt to Space Vector PWM Elucidation for Inverters

Explains the principles of Space Vector Pulse Width Modulation (SVM) for 3-phase inverters including sector identification switching time calculation and comparison to Sinusoidal PWM (SPWM). This aids power electronics engineers in understanding and implementing advanced inverter control. The output is a markdown document.

Output: 

				
					Act as a University Professor of Power Electronics.
Your TASK is to provide a detailed explanation of Space Vector Pulse Width Modulation (SVM) as applied to 3-phase inverters (e.g.
 a standard 2-level
 6-switch inverter as in `{inverter_topology_if_specific}`
 or assume standard if not specified).
The explanation should focus on the `{svm_aspect_to_clarify}` (e.g.
 'Principle of space vector representation'
 'Sector identification logic'
 'Calculation of active vector switching times (Ta
 Tb
 T0)'
 'Implementation of different switching sequences'
 'Overmodulation techniques'
 'Advantages over SPWM').
Indicate if a comparison with Sinusoidal PWM (SPWM) is needed via `{comparison_with_spwm_needed_boolean}` (True/False).

**EXPLANATION OF SPACE VECTOR PWM (Markdown format):**

**1. Introduction to Inverter Control and PWM**
    *   Briefly state the role of PWM in 3-phase inverters (controlling output voltage magnitude and frequency).
    *   Introduce SVM as an advanced PWM technique.

**2. The Concept of Space Vectors** (Address if part of `{svm_aspect_to_clarify}`)
    *   **2.1. Inverter Switching States**: For a 2-level
 3-phase inverter
 there are 2^3 = 8 possible switching states (Sa
 Sb
 Sc for upper switches).
    *   **2.2. Voltage Vectors**: Each switching state corresponds to a specific set of line-to-neutral or line-to-line voltages. These can be represented as vectors in a 2D complex plane (alpha-beta stationary reference frame).
        *   Six active (non-zero) voltage vectors (V1 to V6
 forming a hexagon). Magnitude typically (2/3)Vdc.
        *   Two zero voltage vectors (V0
 V7
 all upper switches ON or all lower switches ON).
    *   **2.3. Reference Voltage Vector (`V_ref`)**: The desired output voltage (sinusoidal in steady-state) is also represented as a rotating space vector `V_ref` in the alpha-beta plane.
        *   Magnitude of `V_ref` controls output voltage amplitude.
        *   Frequency of rotation of `V_ref` controls output frequency.

**3. Principle of Space Vector Modulation**
    *   The core idea: Synthesize the rotating reference vector `V_ref` by averaging two adjacent active voltage vectors and one or both zero vectors over a switching period (Ts).
    *   This is achieved by applying these three (or two active + one zero) vectors for specific durations (Ta
 Tb
 T0) within Ts
 such that: `V_ref * Ts = V_a * Ta + V_b * Tb + V_0 * T0`
    where `Ta + Tb + T0 = Ts`.

**4. Key Steps in SVM Implementation**
    *   **4.1. Sector Identification** (Address if part of `{svm_aspect_to_clarify}`)
        *   The alpha-beta plane is divided into six 60-degree sectors by the active voltage vectors.
        *   Logic to determine which sector `V_ref` currently lies in. This typically involves transforming `V_ref` (from desired 3-phase voltages Varef
 Vbref
 Vcref) into Valpha
 Vbeta components and then using their values and angles.
    *   **4.2. Calculation of Switching Times (Ta
 Tb
 T0)** (Address if part of `{svm_aspect_to_clarify}`)
        *   Once the sector is identified
 `V_ref` is synthesized using the two active vectors forming the boundaries of that sector (e.g.
 V1 and V2 for Sector 1) and zero vectors.
        *   Derivation of formulas for Ta
 Tb
 T0 based on `V_ref` magnitude
 angle
 and Vdc. 
            Example for Sector 1 (V_ref between V1 and V2):
            `Ta = (sqrt(3) * Ts * |V_ref| / Vdc) * sin(60_degrees - theta)`
            `Tb = (sqrt(3) * Ts * |V_ref| / Vdc) * sin(theta)`
            `T0 = Ts - Ta - Tb` 
            (where `theta` is the angle of `V_ref` within the sector).
    *   **4.3. Determining Switching Sequences** (Address if part of `{svm_aspect_to_clarify}`)
        *   How to arrange the application of Va
 Vb
 V0 within Ts to minimize switching frequency
 reduce harmonics
 or balance neutral point voltage (in some topologies).
        *   Common sequences: Symmetric (e.g.
 V0-Va-Vb-V7-Vb-Va-V0) or others.
        *   Translating Ta
 Tb
 T0 into gate signals for the inverter switches (S_a
 S_b
 S_c).

**5. `{svm_aspect_to_clarify}` - Focused Explanation**
    *   Provide a detailed expansion on the specific aspect requested by the user
 using the above foundational information.
    *   Include diagrams (textual descriptions or ASCII art if helpful) or pseudo-code if explaining logic like sector identification or time calculation.

**6. Overmodulation Strategies (if part of `{svm_aspect_to_clarify}` or as advanced topic)**
    *   What happens when `|V_ref|` exceeds the hexagon boundary (linear modulation range)?
    *   Brief discussion of overmodulation region 1 (six-step operation is the limit) and techniques to smoothly transition.

**7. Comparison with Sinusoidal PWM (SPWM) (if `{comparison_with_spwm_needed_boolean}` is True)**
    *   **Advantages of SVM over SPWM**:
        *   Higher DC bus utilization (max output voltage for SVM is `Vdc/sqrt(3)` line-to-neutral
 vs. `Vdc/2` for SPWM
 so about 15% more voltage).
        *   Lower harmonic distortion for the same switching frequency (or same distortion at lower switching frequency).
        *   Better suited for digital implementation.
        *   More flexibility in optimizing switching sequences.
    *   **Disadvantages/Complexity of SVM**:
        *   More complex to understand and implement initially due to vector calculations and sector logic.

**8. Conclusion**
    *   Recap the benefits and typical application areas of SVM.

**IMPORTANT**: The explanation should be clear
 structured
 and mathematically sound where appropriate. If a specific `{inverter_topology_if_specific}` implies variations (e.g.
 multilevel SVM)
 acknowledge this
 but focus on standard 2-level unless specified.
							

AI Prompt to Convert Electrical Engineering Paper from English to German

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: 

				
					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.
							

AI Prompt to Metamaterial Antenna Miniaturization Explained

Explains how metamaterials (e.g. SRRs NRI-TLs AMCs) are used to achieve antenna miniaturization detailing the physical mechanisms and discussing performance trade-offs like bandwidth and efficiency. This helps RF engineers understand advanced antenna design techniques. The output is a text-based explanation.

Output: 

				
					Act as a Research Scientist in Applied Electromagnetics and RF Engineering.
Your TASK is to explain how metamaterials
 specifically focusing on `{metamaterial_type_for_focus}` (e.g.
 'Engineered Magnetic Substrates using Split-Ring Resonators (SRRs)'
 'Negative Refractive Index Transmission Line (NRI-TL) sections'
 'Artificial Magnetic Conductors (AMCs) as ground planes'
 'Zero-Order Resonators (ZORs)')
 are used to achieve miniaturization of a specific `{antenna_type_to_miniaturize}` (e.g.
 'patch antenna'
 'dipole antenna'
 'monopole antenna'
 'IFA - Inverted-F Antenna').
The explanation should emphasize the `{explanation_focus_area_csv}` (e.g.
 'Physical_mechanism_for_size_reduction
Impact_on_resonant_frequency
Bandwidth_and_Q-factor_trade-offs
Efficiency_considerations
Practical_implementation_challenges').

**EXPLANATION OF METAMATERIAL-BASED ANTENNA MINIATURIZATION:**

**1. Introduction to Antenna Miniaturization and Metamaterials:**
    *   Briefly state the need for antenna miniaturization in modern electrical engineering (e.g.
 mobile devices
 IoT
 wearables).
    *   What are metamaterials? (Artificial structures with engineered electromagnetic properties not found in nature
 e.g.
 negative permittivity/permeability
 high effective refractive index).

**2. Focus on `{metamaterial_type_for_focus}` for Miniaturizing `{antenna_type_to_miniaturize}`:**
    *   **2.1. Description of `{metamaterial_type_for_focus}`:**
        *   What is its typical structure (e.g.
 periodic arrangement of SRRs
 unit cells of series capacitors and shunt inductors for NRI-TL
 mushroom-like AMC structures)?
        *   What unique electromagnetic property does it exhibit that is leveraged for miniaturization (e.g.
 high effective permeability `mu_eff > mu_0` below SRR resonance
 left-handed behavior for NRI-TL
 in-phase reflection for AMC)?
    *   **2.2. Integration with `{antenna_type_to_miniaturize}`:**
        *   How is the `{metamaterial_type_for_focus}` typically incorporated into or near the `{antenna_type_to_miniaturize}`? (e.g.
 as a substrate material
 as a ground plane
 loaded onto the radiating element
 as part of the feed structure).

**3. Explanation of Key Aspects (`{explanation_focus_area_csv}`):**
    *   **3.1. Physical Mechanism for Size Reduction / Impact on Resonant Frequency:**
        *   Explain in detail HOW the metamaterial interaction leads to a reduction in the antenna's physical size for a given resonant frequency
 OR how it lowers the resonant frequency for a given physical size.
            *   _If `{metamaterial_type_for_focus}` is SRR-based magnetic substrate for a patch_: High `mu_eff` increases effective inductance
 `f_res ~ 1/sqrt(LC)`. Or
 it increases effective refractive index `n_eff = sqrt(eps_eff * mu_eff)`
 making electrical length `n_eff * physical_length` larger
 so physical length can be smaller.
            *   _If NRI-TL (or Composite Right/Left-Handed - CRLH TL) based_: Can achieve resonance at very low frequencies (even zero frequency for ZOR) independent of physical length due to left-handed phase characteristics
 allowing for electrically small antennas.
            *   _If AMC ground plane for a monopole/PIFA_: AMC provides in-phase reflection
 allowing antenna to be placed very close to the ground plane (e.g.
 < lambda/4)
 unlike a Perfect Electric Conductor (PEC) which requires lambda/4 spacing for image to add in phase. This reduces overall height.
    *   **3.2. Bandwidth and Q-Factor Trade-offs:**
        *   Discuss the fundamental relationship between antenna size
 Q-factor
 and bandwidth (Chu-Wheeler limit). Miniaturization often leads to higher Q and narrower bandwidth.
        *   How does the use of `{metamaterial_type_for_focus}` specifically affect the antenna's bandwidth? Are there techniques to mitigate bandwidth reduction (e.g.
 coupling multiple resonators
 using lossy metamaterials strategically)?
    *   **3.3. Efficiency Considerations:**
        *   What are the primary loss mechanisms in metamaterial-based antennas (e.g.
 conductor losses in small resonant structures of metamaterial unit cells
 dielectric losses in substrates
 radiation efficiency changes)?
        *   How does the efficiency of the miniaturized antenna compare to its conventional counterpart or other miniaturization techniques?
    *   **3.4. Practical Implementation Challenges:**
        *   Fabrication tolerances (metamaterials often require precise dimensions
 especially at higher frequencies).
        *   Sensitivity to environmental factors.
        *   Complexity of design and simulation due to intricate structures.
        *   Achieving desired metamaterial properties over a sufficient bandwidth for the antenna operation.

**4. Example Application or Illustrative Design (Conceptual):**
    *   Briefly describe a conceptual example of a `{antenna_type_to_miniaturize}` miniaturized using `{metamaterial_type_for_focus}`
 highlighting how the principles translate into a physical antenna.

**5. Conclusion:**
    *   Summarize the potential and limitations of using `{metamaterial_type_for_focus}` for antenna miniaturization in electrical engineering.

**IMPORTANT**: The explanation should be grounded in electromagnetic theory. Focus on providing physical insight rather than just stating facts. Address all areas mentioned in `{explanation_focus_area_csv}`.
							

AI Prompt to Simplify Electrical Jargon for Non-Engineers

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: 

				
					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.
							

AI Prompt to Fractional-N PLL Phase Noise Sources Analysis

Explains the origin and impact of various noise sources (e.g. reference spurs DSM quantization VCO noise charge pump noise) in a Fractional-N Phase-Locked Loop (PLL) synthesizer and how they contribute to output phase noise. This helps RF/mixed-signal engineers in designing low-noise frequency synthesizers. The output is a markdown report.

Output: 

				
					Act as a Specialist in RFIC Design and Phase-Locked Loops.
Your TASK is to explain the origin
 characteristics
 and impact of key noise sources on the output phase noise of a Fractional-N Phase-Locked Loop (PLL) synthesizer.
Consider the general `{pll_architecture_details_text}` (e.g.
 'Typical charge-pump PLL with a multi-modulus divider and a 3rd-order Delta-Sigma Modulator (DSM) for fractional division'
 'Integer-N PLL with fractional capability via dithering' - though focus on DSM based).
Pay particular attention to the `{key_noise_source_to_focus_on}` (e.g.
 'Delta-Sigma Modulator quantization noise'
 'Charge pump current mismatch and timing errors'
 'VCO phase noise'
 'Reference input phase noise'
 'Loop filter noise')
 and its behavior across the specified `{output_frequency_range_ghz}`.

**ANALYSIS OF PLL PHASE NOISE SOURCES (Markdown format):**

**1. Introduction to Fractional-N PLLs and Phase Noise**
    *   Brief overview of Fractional-N PLL function: Synthesizing output frequencies that are non-integer multiples of the reference frequency
 enabling fine frequency resolution.
    *   Importance of low phase noise in communication systems
 ADCs/DACs
 etc. Definition of phase noise L(f_offset).
    *   Mention of the `{pll_architecture_details_text}` as the context.

**2. General Model of Noise Contributions in a PLL**
    *   Concept of noise transfer functions: How noise from each component (Reference
 PFD/CP
 Loop Filter
 VCO
 Divider/DSM) is shaped and appears at the PLL output.
    *   In-band noise (typically dominated by reference
 PFD/CP
 DSM
 loop filter) vs. out-of-band noise (typically dominated by VCO). Loop bandwidth (`omega_L`) is critical.

**3. Detailed Analysis of `{key_noise_source_to_focus_on}`**
    *   **3.1. Origin and Physical Mechanism of `{key_noise_source_to_focus_on}`:**
        *   _If DSM quantization noise_: Explain how the DSM's process of approximating the fractional division ratio introduces quantization error. Shape of this noise (e.g.
 high-pass shaped by DSM order).
        *   _If Charge Pump noise_: Current mismatch between UP/DOWN pulses
 clock feedthrough
 charge sharing
 thermal noise in CP transistors. Leads to phase errors when PFD output is non-zero (even small phase error can cause CP to pulse).
        *   _If VCO phase noise_: Intrinsic oscillator noise (thermal
 flicker noise in active devices
 tank losses). Typically modeled by Leeson's formula or similar
 showing 1/f^3
 1/f^2
 and noise floor regions.
        *   _If Reference noise_: Phase noise of the crystal oscillator or other reference source.
        *   _If Loop Filter noise_: Thermal noise from resistors in the loop filter.
    *   **3.2. Characteristics and Spectral Shape of `{key_noise_source_to_focus_on}`:**
        *   How does this noise source typically appear in the frequency domain (e.g.
 flat
 1/f
 shaped)?
        *   Its dependence on PLL parameters (e.g.
 DSM order
 CP current
 VCO tank Q
 loop filter component values).
    *   **3.3. Transfer Function to Output Phase Noise:**
        *   Describe (qualitatively or with simplified equations) how the noise from `{key_noise_source_to_focus_on}` is filtered by the PLL loop dynamics to contribute to the output phase noise.
            *   Noise sources inside the loop (PFD/CP
 LF
 VCO
 DSM) are generally low-pass filtered by the closed-loop response for their contribution to output phase _within_ the loop bandwidth
 and high-pass filtered for their contribution to output phase _outside_ the loop bandwidth (VCO noise is a key example of this). No
 this is not quite right. 
            *   Reference and PFD/CP noise typically see a low-pass transfer function to the output (multiplied by N_total). 
            *   VCO noise sees a high-pass transfer function to the output.
            *   DSM noise is injected at the divider
 its transfer function to the output is complex but generally shaped by the loop; often appears as in-band noise and spurs.
    *   **3.4. Impact on Output Phase Noise across `{output_frequency_range_ghz}`:**
        *   Does the contribution of `{key_noise_source_to_focus_on}` change significantly with output frequency (e.g.
 VCO noise often degrades at higher frequencies)?
        *   How does it affect different offset frequency regions (e.g.
 close-in phase noise vs. far-out noise floor)?
    *   **3.5. Mitigation Techniques for `{key_noise_source_to_focus_on}`:**
        *   Common design techniques to reduce its impact (e.g.
 for DSM noise: higher order DSM
 careful sequence design
 increasing PFD frequency; for CP noise: current calibration
 careful layout
 larger CP currents; for VCO noise: high-Q tank
 low-noise biasing
 optimal device sizing).

**4. Interaction with Other Noise Sources**
    *   Briefly discuss how the dominance of `{key_noise_source_to_focus_on}` might change depending on the loop bandwidth choice and other component specifications.
    *   Overall PLL phase noise is the sum of contributions from all sources.

**5. Conclusion**
    *   Summarize the importance of understanding and mitigating `{key_noise_source_to_focus_on}` for achieving low-noise Fractional-N PLL performance.

**IMPORTANT**: The explanation should be technically deep yet clear. Focus on providing insight into the behavior and impact of the specified noise source. Use block diagrams conceptually if it aids explanation (describe them).
							

AI Prompt to Adapt Electrical Engineering Report for International Audience

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: 

				
					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.
							

AI Prompt to Translate PLC Ladder Logic Comments

Translates inline comments from a PLC ladder logic program snippet from a specified source language to a target language while preserving the context of the electrical control logic. This aids in international collaboration and understanding of legacy code. The output is the code snippet with translated comments.

Output: 

				
					Act as a Bilingual Automation Engineer with expertise in PLC programming.
Your TASK is to translate the inline comments within the provided `{plc_ladder_logic_snippet_with_comments_text}` from `{source_language_code}` (e.g.
 'de' for German
 'ja' for Japanese
 'zh-CN' for Simplified Chinese) to `{target_language_code}` (e.g.
 'en' for English).
The `{plc_ladder_logic_snippet_with_comments_text}` will be a text representation of ladder logic
 where comments are clearly associated with rungs
 contacts
 coils
 or instructions.

**TRANSLATION PROCESS AND OUTPUT:**

1.  **Identify Comments**: Parse the `{plc_ladder_logic_snippet_with_comments_text}` to locate all comments. Comments might be prefixed (e.g.
 '//'
 ';'
 '#') or on separate lines clearly associated with a logic element or rung.
2.  **Contextual Translation**: For each comment:
    *   Understand its meaning in the context of the surrounding ladder logic elements (inputs
 outputs
 timers
 counters
 instructions). The comment often describes the PURPOSE or CONDITION of that part of the logic.
    *   Translate the comment from `{source_language_code}` to `{target_language_code}`
 ensuring that the technical meaning and relevance to the electrical control logic are preserved. Use appropriate technical terminology in the target language.
    *   AVOID literal translations that might be grammatically correct but technically ambiguous or misleading in an electrical engineering context.
3.  **Reconstruct Snippet**: Reconstruct the ladder logic snippet
 replacing the original comments with their translated versions. The structure and logic of the ladder diagram itself MUST remain UNCHANGED.

**Output Format:**
The output MUST be the complete `{plc_ladder_logic_snippet_with_comments_text}` with all original comments translated into the `{target_language_code}`
 in plain text.

**Example Input (`{plc_ladder_logic_snippet_with_comments_text}`
 with German comments
 `{source_language_code}`='de'
 `{target_language_code}`='en'):**
`RUNG 001
|--| |----|/|----( )-- ; Sensor_Eingang_Aktiv
|  X001   X002    Y001   ; Motor_Starten_wenn_Schutz_OK
|                               ; UND_Sensor_Aktiv
`

**Example Output (Translated to English):**
`RUNG 001
|--| |----|/|----( )-- ; Sensor_Input_Active
|  X001   X002    Y001   ; Start_Motor_if_Safety_Guard_OK
|                               ; AND_Sensor_Active
`

**IMPORTANT**: The accuracy of the technical translation of the comments is paramount. The ladder logic code itself should not be altered. If the input format of comments is complex (e.g.
 multi-line comments spanning specific blocks)
 maintain that structure in the output.
							

AI Prompt to Summarize Latest Electrical Engineering Research Trends

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: 

				
					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.
							

AI Prompt to Identify Knowledge Gaps in Electrical Engineering Literature

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: 

				
					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.
							

AI Prompt to Generate Bibliography of Seminal Papers

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: 

				
					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.
							
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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. Megan Clay

      is the AIs effectiveness in generating prompts largely dependent on the quality of input data?

    2. Lance

      engineering projects also ? Lets discuss that too.

      1. Fabrice

        AI isnt a magic fix-all solution!

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