GPT-4 Prompts

Code completion

1. Complete the following function [function_name], which takes in [input_variable] and returns [output_variable].
2. Suggest alternative implementation(s) for the following function [function_name].
3. Provide code completion for the following line of code [code_snippet].
4. Suggest code snippets to replace the following block of code [code_block].
5. Given the following input data [input_data], generate code to preprocess the data.
6. Generate code to implement the following algorithm [algorithm_name].
7. Generate code to scrape the following website [website_url].
8. Suggest code snippets to optimize the following code for performance.
9. Suggest code snippets to improve the readability of the following code.

Code summarization

1. Summarize the functionality of the following codebase [codebase_url].
2. Summarize the differences between the following two versions of the code [code_version_1] and [code_version_2].
3. Summarize the purpose of the following function [function_name].
4. Summarize the structure of the following dataset [dataset_name].
5. Summarize the output of the following machine learning model [model_name].
6. Summarize the results of the following A/B test [test_name].
7. Summarize the key features of the following API [api_name].
8. Summarize the main components of the following system architecture [architecture_name].
9. Summarize the input and output of the following function [function_name].
10. Summarize the main tasks performed by the following program [program_name].

Refactoring assistance

1. Suggest a more efficient way to implement the following function [function_name].
2. Suggest a more modular structure for the following codebase [codebase_name].
3. Suggest a more readable way to format the following code [code_snippet].
4. Suggest a more secure way to implement the following API [api_name].
5. Suggest a more robust error handling strategy for the following program [program_name].
6. Suggest a more optimized way to implement the following algorithm [algorithm_name].
7. Suggest a more scalable way to implement the following system architecture [architecture_name].
8. Suggest a more flexible way to implement the following database schema [database_schema].
9. Suggest a more user-friendly way to implement the following user interface [ui_name].
10. Suggest a more extensible way to implement the following plugin [plugin_name].

Bug detection

1. Identify potential bugs in the following code snippet [code_snippet].
2. Identify potential security vulnerabilities in the following API [api_name].
3. Identify potential performance issues in the following program [program_name].
4. Identify potential errors in the following algorithm [algorithm_name].
5. Identify potential data quality issues in the following dataset [dataset_name].
6. Identify potential compatibility issues in the following library [library_name].
7. Identify potential scalability issues in the following system architecture [architecture_name].
8. Identify potential usability issues in the following user interface [ui_name].
9. Identify potential maintenance issues in the following codebase [codebase_name].
10. Identify potential versioning issues in the following program [program_name].

Code generation

1. Generate code to implement the following function [function_name].
2. Generate code to preprocess the following dataset [dataset_name].
3. Generate code to perform the following machine learning task [task_name].
4. Generate code to scrape the following website [website_url].
5. Generate code to optimize the following algorithm [algorithm_name].
6. Generate code to implement the following system architecture [architecture_name].
7. Generate code to perform the following database query [query_name].
8. Generate code to visualize the following dataset [dataset_name]

Data cleaning

1. Clean the following dataset [dataset_name] by removing missing values.
2. Clean the following dataset [dataset_name] by correcting errors in the data.
3. Clean the following dataset [dataset_name] by transforming the data to a more suitable format.
4. Clean the following dataset [dataset_name] by removing duplicates.
5. Clean the following dataset [dataset_name] by normalizing the data.
6. Clean the following dataset [dataset_name] by performing outlier detection.
7. Clean the following dataset [dataset_name] by handling null values.

Data preprocessing

1. Preprocess the following dataset [dataset_name] by normalizing the data.
2. Preprocess the following dataset [dataset_name] by scaling the data.
3. Preprocess the following dataset [dataset_name] by applying feature engineering.
4. Preprocess the following dataset [dataset_name] by performing dimensionality reduction.
5. Preprocess the following dataset [dataset_name] by handling categorical variables.
6. Preprocess the following dataset [dataset_name] by encoding text data.

Data analysis

1. Analyze the following dataset [dataset_name] and provide summary statistics.
2. Analyze the following dataset [dataset_name] and identify correlations between variables.
3. Analyze the following dataset [dataset_name] and visualize the data using appropriate plots.
4. Analyze the following dataset [dataset_name] and perform hypothesis testing.
5. Analyze the following dataset [dataset_name] and identify trends in the data.
6. Analyze the following dataset [dataset_name] and identify anomalies in the data.

Machine learning

1. Train a machine learning model to perform the following task [task_name].
2. Tune hyperparameters of the following machine learning model [model_name].
3. Evaluate the performance of the following machine learning model [model_name].
4. Identify the best machine learning model to use for the following task [task_name].
5. Perform feature selection for the following machine learning task [task_name].

Natural language processing

1. Generate a summary of the following text document [document_name].
2. Generate a translation of the following text document [document_name].
3. Generate a paraphrase of the following text document [document_name].
4. Identify the sentiment of the following text document [document_name].
5. Identify the named entities in the following text document [document_name].

Computer vision

1. Identify objects in the following image [image_name].
2. Segment the following image [image_name] into different regions.
3. Generate a caption for the following image [image_name].
4. Classify the following image [image_name] into different categories.
5. Detect faces in the following image [image_name].

Speech recognition

1. Transcribe the following speech recording [recording_name].
2. Identify the speaker in the following speech recording [recording_name].
3. Generate a summary of the following speech recording [recording_name].
4. Detect the emotion of the speaker in the following speech recording [recording_name].
5. Perform speaker diarization for the following speech recording [recording_name].

Chatbot development

1. Develop a chatbot to answer questions related to the following topic [topic_name].
2. Train a chatbot to understand natural language queries related to the following topic [topic_name].
3. Develop a chatbot to provide customer support for the following product/service [product_name].
4. Develop a chatbot to schedule appointments for the following service [service_name].
5. Develop a chatbot to provide recommendations for the following product/service [product_name].

Recommender systems

1. Develop a recommender system that can recommend items to users based on their past behavior [past_behavior].
2. Train a recommender system to recommend movies to users based on their ratings for similar movies [similar_movies].
3. Improve the recommender system's accuracy by incorporating user demographics [user_demographics] into the model.

Data visualization

1. Visualize the following dataset [dataset_name] using the following visualization tool [visualization_tool].
2. Develop an interactive dashboard that can help users visualize the following data [data] for [variable_1].
3. Identify patterns and trends in the following dataset [dataset_name] using the following visualization technique [visualization_technique].

Web development

1. Develop a web application that can perform the following task [task_name] for [variable_1].
2. Optimize the performance of the following web application [web_app_name] by reducing load times for [variable_2].
3. Improve the user experience of the following web application [web_app_name] by redesigning the user interface [UI_redesign].

Database management

1. Optimize the following database [database_name] by creating indexes for [variable_1].
2. Design a new database schema for the following task [task_name] that can handle large amounts of data [large_data].
3. Backup the following database [database_name] to a remote server [remote_server].

Cybersecurity

1. Identify potential vulnerabilities in the following system [system_name] based on [variable_1].
2. Develop a secure login system that can prevent the following types of attacks [attack_types].
3. Train a model that can detect the following types of attacks [attack_types] based on network traffic [network_traffic].

DevOps

1. Develop a pipeline that can automatically build and deploy the following code [code] to the following environment [environment].
2. Optimize the following pipeline [pipeline_name] by reducing build times for [variable_1].
3. Monitor the following system [system_name] for potential issues using the following tool [monitoring_tool].

Cloud computing

1. Migrate the following application [application_name] to the cloud for [variable_1].
2. Optimize the following cloud infrastructure [cloud_infrastructure] by reducing costs for [variable_2].
3. Train a machine learning model on the following cloud platform [cloud_platform] using the following dataset [dataset_name].

Natural language processing

1. Develop a sentiment analysis model that can identify the sentiment of the following text [text] for [variable_1].
2. Train a named entity recognition model to identify the following entities [entities] in the following text [text].
3. Generate paraphrases of the following sentence [sentence] for [variable_2].

Image processing

1. Identify the following object(s) [objects] in the following image [image_name] for [variable_1].
2. Develop an image classification model that can classify the following images [images] into the following categories [categories].
3. Improve the accuracy of the following object detection model [model_name] by fine-tuning the model with additional data [additional_data].

Machine learning

1. Develop a machine learning model that can predict the following outcome [outcome] based on the following input variables [input_variables].
2. Optimize the following machine learning model [model_name] by reducing its training time for [variable_1].
3. Explain the following machine learning model's [model_name] predictions using the following interpretability technique [interpretability_technique].

Robotics

1. Develop a robot that can perform the following task [task_name] using the following sensors [sensors].
2. Optimize the following robot's [robot_name] movements by reducing the energy consumption for [variable_1].
3. Train a reinforcement learning model to control the following robot's [robot_name] movements in the following environment [environment].

Augmented reality

1. Develop an augmented reality application that can overlay the following information [information] onto the following image [image_name].
2. Optimize the following augmented reality application's [app_name] performance by reducing its latency for [variable_1].
3. Train an object recognition model to recognize the following object(s) [objects] in the following augmented reality environment [environment].

Virtual reality

1. Develop a virtual reality simulation that can simulate the following environment [environment] for the following user(s) [users].
2. Optimize the following virtual reality application's [app_name] graphics quality by reducing its render times for [variable_1].
3. Train a reinforcement learning model to control the following virtual reality simulation's [simulation_name] environment based on user input [user_input].

Natural language generation

1. Generate the following text [text] based on the following input data [input_data].
2. Develop a summarization model that can summarize the following text [text] for [variable_1].
3. Generate a personalized message for the following user(s) [users] based on their profile information [profile_info].

Speech recognition

1. Identify the following words [words] in the following audio file [audio_file_name] for [variable_1].
2. Train a speech recognition model to transcribe the following speech [speech] for [variable_2].
3. Improve the speech recognition model's accuracy by incorporating the following user accent(s) [accents] into the model.

Optimization

1. Optimize the following system [system_name] by reducing its latency for [variable_1].
2. Develop a scheduling algorithm that can schedule the following tasks [tasks] for the following resources [resources].
3. Train a reinforcement learning model to optimize the following process [process_name] based on user feedback [user_feedback].

Cloud computing security

1. Develop a secure cloud infrastructure that can prevent the following types of attacks [attack_types] for [variable_1].
2. Train a model that can detect potential security threats in the following cloud environment [cloud_environment] for [variable_2].

Cybersecurity

1. Develop a cybersecurity model that can detect the following types of attacks [attack_types] for [variable_1].
2. Train a machine learning model to identify potential vulnerabilities in the following codebase [codebase] for [variable_2].
3. Develop a secure authentication system that can authenticate the following user(s) [users] for [variable_3].

Blockchain

1. Develop a blockchain application that can perform the following task [task_name] for [variable_1].
2. Train a machine learning model to detect potential fraud in the following blockchain transactions [transactions] for [variable_2].
3. Improve the following blockchain's [blockchain_name] scalability by reducing its transaction confirmation time for [variable_3].

IoT

1. Develop an IoT application that can control the following device(s) [devices] for [variable_1].
2. Train a machine learning model to predict the following outcome [outcome] based on the following IoT data [iot_data].
3. Optimize the following IoT system's [system_name] power consumption by reducing its energy usage for [variable_2].

DevOps

1. Develop a CI/CD pipeline that can deploy the following codebase [codebase] to the following environment [environment] for [variable_1].
2. Train a machine learning model to predict the following system's [system_name] resource utilization based on historical data [historical_data].
3. Optimize the following system's [system_name] deployment process by reducing its deployment time for [variable_2].

Cloud computing

1. Develop a cloud infrastructure that can handle the following amount of traffic [traffic] for [variable_1].
2. Train a machine learning model to predict the following cloud infrastructure's [cloud_infrastructure] resource utilization based on historical data [historical_data].
3. Optimize the following cloud infrastructure's [cloud_infrastructure] performance by reducing its response time for [variable_2].

Data analysis

1. Develop a data analysis model that can analyze the following data [data] for [variable_1].
2. Train a machine learning model to predict the following outcome [outcome] based on the following data [data].
3. Optimize the following data analysis model's [model_name] performance by reducing its processing time for [variable_2].

Data visualization

1. Develop a data visualization tool that can visualize the following data [data] for [variable_1].
2. Train a machine learning model to classify the following data points [data_points] into the following categories [categories].
3. Optimize the following data visualization tool's [tool_name] performance by reducing its rendering time for [variable_2].

Chatbots

1. Develop a chatbot that can answer the following questions [questions] for [variable_1].
2. Train a machine learning model to classify the following user messages [user_messages] into the following categories [categories].
3. Optimize the following chatbot's [chatbot_name] performance by reducing its response time for [variable_2].

Mobile app development

1. Develop a mobile application that can perform the following task [task_name] for [variable_1].
2. Train a machine learning model to predict the following outcome [outcome] based on the following mobile application data [mobile_data].
3. Optimize the following mobile application's [app_name] performance by reducing its load time for [variable_2].

Web development

1. Develop a web application that can perform the following task [task_name] for [variable_1].
2. Train a machine learning model to predict the following outcome [outcome] based on the following web application data [web