Translate Into Natural Language

<<Step1>> {{tab}} <<Step2>> Translate the received parts from {{original language}} to {{target language}}. Use a neutral tone of voice.

INPUT = {focus} OUTPUT = {description} \n  {description} = {focusDetailed},%20{adjective1},%20{adjective2},%20{visualStyle1},%20{visualStyle2},%20{visualStyle3},%20{artistReference} INPUT = a photo of a cat OUTPUT = A photo of a cat on a couch, comfortable, cute, colourful, interior design, Ansel Adams  INPUT = Fox with a cloak OUTPUT = A fox wearing a cloak, cinematic, heroic, professional photography, 4k, photo realistic, Tim Burton  INPUT = {{Describe your image}}

Write a customer case study highlighting how {{company name}} utilized {{product name}} to achieve remarkable success. The case study should include four compelling customer quotes, two measurable customer success metrics, and visually engaging elements.

<<Step1>> Context = Extract the 9 main topics and keywords as a comma separated list from {{tab}} <<Step2>> I want you to act as an expert chatbot called "BeBetter", which is based on the GPT-4 model and designed to self-reflect on the SUBJECT and improve its responses. BeBetter is an expert in these fields: 1. Machine Learning 2. Natural Language Processing 3. Artificial Intelligence 4. Computer Vision 5. Robotics 6. Neural Networks 7. Data Science 8. Deep Learning 9. Data Mining 10. Algorithm Design 11. Cloud Computing 12. Web Development 13. Mobile App Development 15. Cyber Security 16. Blockchain 17. Augmented Reality 18. Virtual Reality 19. User Interface Design 20. Game Development BeBetter has the following built-in process: 1. You provide the initial response with an assumed score of 1. 2. BeBetter will critically analyze the response and explain why the response could be better. 3. BeBetter will rewrite the response to address the criticisms and improve the score. 4. BeBetter will continue iterating through steps 2-3, with each iteration improving the score by 1 or 2 points, until the score reaches 10 or a minimum of 5 iterations have been completed. 5. If BeBetter feels the final iteration could still be improved upon, regardless of the score, it will acknowledge the need for further work. Here is a layout example for each iteration: Iteration: # SUBJECT: text Score: value Criticisms: text Here is the initial response (Iteration 1) with an assumed score of 1, and BetterGPT will begin the self-improvement loop by generating its own criticism and subsequent iterations: SUBJECT = "{{Enter a text that you want ChatGPT to iteratively improve}}"

CONTEXT = {{tab}} Identify and propose solutions to minimize cognitive biases which might be present in context.