How to use OpenAI's ChatGPT to classify customer feedback

Learn how to use OpenAI and GPT-3 inside the spreadsheet to classify customer feedback in Customer Support and Customer Success operations.

How to use OpenAI in Rows

OpenAI template

There's a near-infinite amount of tasks you can solve using OpenAI. Follow this guide on how to set up the integration and use this template showcase to get started with 10 pre-built examples, follow along the list, or watch our video tutorial.

Connecting the OpenAI integration in Rows

You can find the OpenAI integration by browsing the integrations gallery and searching for "OpenAI".

OpenAI integration in gallery

To connect the integration and use the power of AI inside Rows all you need is an API Key. You can get your API key by going to the View API Keys option on your OpenAI account. If you don't have an account yet, sign-up here. All free accounts have API access.

API Key panel in OpenAI

Now simply copy the API key, go to the OpenAI integration page, press Connect, paste it and click Connect. Your Rows workspace is now connected to your OpenAI account and you're ready to go.

OpenAI integration page

Using the OpenAI functions

The OpenAI integration comes with five proprietary functions that automate prompts to address specific types of tasks:

  • ASK_OPENAI(), which aims at leveraging the power of GPT to solve general tasks.
  • CREATE_LIST_OPENAI(), which is designed specifically to create tables and list of dummy data, for testing purposes.
  • CLASSIFY_OPENAI(), which is designed specifically to classify texts into a given set of tags.
  • TRANSLATE_OPENAI(), which translates texts from/into a wide range of languages.
  • APPLY_TASK_OPENAI(), which is designed specifically to clean up or apply logic rules to data.

You can use them via the Autocomplete in the editor,

ASK_OPENAI on the editor

or via the Actions wizard:

Screenshot 2023-04-19 at 16.58.52

All OpenAI functions need to be configured through mandatory and optional parameters, depending on their purpose. Let's go through them one by one.


The prompt is the instruction to give to the model in our most generic function ASK_OPENAI(). This is where you'll enter the "ask" you want the AI to answer. You can use the prompt to solve a task by explicitly writing it in prose. Example:

1=ASK_OPENAI("Generate 100-word paragraph about the latest iPhone release")

Ask anything to OpenAI

Tips for creating Prompts

The Open AI integration uses its Completions capability, which means that the artificial intelligence model will predict the next word(s) that follow the prompt. With that in mind, here are a few tips on how to construct the right prompt for your task:

  • Be specific: The more specific the prompt, the most likely it is to get the intended result. If you're looking for the Population of the country in millions, "The Population of France, in millions is: " is a better prompt than simply "The Population of France".
  • Give examples: You can train the model on the type of answer you're looking for. If you are using Open AI for text classification, use the prompt to give a couple of examples of inputs and expected outputs. For example: "Categorize job title by function name. Head of Marketing:Marketing, COO:C-Level, CMO: "
  • Phrase the end of the prompt as the start of the answer: The model will answer with a direct continuation to the prompt. Use that insight to end the prompt with the structure you expect from the answer. If you want to use the OpenAI integration to summarize text, be clear on how to start. Example: "What are 2 main takeaways from this review: ",A2(cell reference with the product review)," ? Summarize it into 2 bullet points. Main takeaways: ")

Temperature (optional)

The temperature is common to all functions and is used to fine tune the sampling temperature, varying between 0 and 1. Use 1 for creative applications, and 0 for well-defined straight answers.

If you're doing tasks that require a factual answer (e.g. country populations, capitalize text), then 0 (the default) is a better fit. If you're using the AI for tasks where there aren't definite answers - such as generating text, summarizing text, or translating - then experiment with a higher temperature, which allows the engine to better capture text nuances and idiomatic expressions.

Max_tokens (optional)

This max_tokens represents the maximum number of tokens to generate in the completion. It's present in all OpenAI functions. You can think of tokens as pieces of words. Here are a few helpful rules of thumb examples from the OpenAI Help center:

  • 1 token ~= 4 chars in English
  • 1 token ~= 3/4 words
  • 100 tokens ~= 75 words
  • 1-2 sentences ~= 30 tokens
  • 1 paragraph ~= 100 tokens
  • 1,500 words ~= 2048 tokens

You can use any number starting with 0. The default value is 200. Most models have a context length of 2048 tokens, except for the newest models which support a maximum of 4096. For tasks that require more text output - text generation/summarization/translation - pick a higher value (e.g. 250).

Model (optional)

The AI model to use to generate the answer. It can be chosen in both functions, and by default, it uses "gpt-3.5-turbo". Below you find a list of all of the available GPT-3.5 models:

gpt-3.5-turboMost capable GPT-3.5 model and optimized for chat at 1/10th the cost of text-davinci-003. Will be updated with our latest model iteration.4,096 tokensUp to Sep 2021
gpt-3.5-turbo-0301Snapshot of gpt-3.5-turbo from March 1st 2023. Unlike gpt-3.5-turbo, this model will not receive updates, and will only be supported for a three month period ending on June 1st 2023.4,096 tokensUp to Sep 2021
text-davinci-003Can do any language task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models. Also supports inserting completions within text.4,097 tokensUp to Jun 2021
text-davinci-002Similar capabilities to text-davinci-003 but trained with supervised fine-tuning instead of reinforcement learning4,097 tokensUp to Jun 2021

Number of items (optional)

The number of items is available only in the CREATE_LIST_OPENAI() function, and represents the expected number of items in the list.

1=CREATE_LIST_OPENAI("Full names and email address",5,,500)

Tags and multi-tag (optional)

The tags and multi-tag properties are available only in the CLASSIFY_OPENAI() function. The first is mandatory and represents the categories you want your text to be classified into.

For example, if you need to classify a list of product reviews in column A, into positive, neutral, negative and very negative, you just need to input those tags separated by a coma, as follows:

1=CLASSIFY_OPENAI(A2, "positive, neutral, negative, very negative")

The second is optional and can be "true" (default) or "false". If true, the function can use more than one tag to classify your text. If false, it will only use one tag. Use false when you need a mutually exclusive strict categorization.


The language is available only in the TRANSLATE_OPENAI() function, and indicates the destination language for your translation tasks. Use the function as follows:


Task and text

The task and text are available only in the APPLY_TASK_OPENAI() function, and are used to specify the logic rule to some text.

For example, if you need to capitalize a string of text, use the function as follows:

1=APPLY_TASK_OPENAI("Capitalize all letters", "i HavE a doG")

Examples of CLASSIFY_OPENAI to classify customer feedback

There are several ways to use OpenAI to classify customer feedback:

  • NPS surveys: Tag responses to open-ended questions on NPS surveys into features, customer requests and product themes.
  • Feedback forms: Classify feedback form responses from customers into most requested features, issues and bugs.
  • Support emails: Tag customer support emails into the topic of the issue faced by the customer.
  • Classify sentiment in social media comments: Automatically classify the social media comments on your pages based on sentiment, emotions and opinions.

Let's go through each of them.

Tag NPS Survey responses

Goal: Tag responses to open-ended questions on NPS surveys into features, customer requests and product themes.


1=CLASSIFY_OPENAI(A2,"Improvements, bugs, general feedback, need expansion, performance issues",,1,200)


  • Add the NPS survey response as a first argument (here, cell A2)
  • Specify the list of tags as a second argument
  • Set a temperature of 1 to better capture language nuances
  • Leavemulti-tag blank (default true) to allow for multiple tagging

NPS Survey

Tags for text classification

The pre-trained LLMs (Large language models) such as GPT-3 can be extremely helpful at solving generic text classification tasks. However, to guarantee that the model classifies the customer feedback from a specific list of keywords that are the most relevant to your use, you need to specify a closed list of tags in the tags argument of our CLASSIFY_OPENAI() function.

Classify product themes in feedback forms

Sometimes you need to classify customer feedback into a pre-set list of options to simplify its reporting or to integrate smoothly into your CRM or downstream workflow. In this example we'll use the model to classify responses to a feedback form into a predefined list of tags.

Goal: Classify feedback from customers into a list of product themes or areas


1=CLASSIFY_OPENAI(A2,"Delivery, Food selection, Customer Service, Price, Quality",,1,500)


  • Add the feedback as a first argument (here, cell A2)
  • Specify the list of tags as a second argument
  • Set a temperature of 1 to better capture language nuances
  • Leave multi-tag blank (default true) to allow for multiple tagging

Feedback form responses

Classify support emails for triage

Triaging emails is an important aspect of Customer Support operations both for the allocation of the agents, and for performance reporting. You can use the OpenAI integration to automatically tag customer support emails.

Goal: Tag customer support emails based issue type for triage and reporting.


1=CLASSIFY_OPENAI(A2,"Order Status, Shipping, Return Policy, General Food, Defect, Price",,1,500)


  • Add the support email as a first argument (here, cell A2)
  • Specify the list of tags as a second argument
  • Set a temperature of 1 to better capture language nuances
  • Leave multi-tag blank (default true) to allow for multiple tagging.

Support emails

Classify sentiment in social media comments

Goal: Analyze comments from social media accounts and extract the sentiment from the text.


1=CLASSIFY_OPENAI(A2,"Positive, Neutral, Negative",false,1,500)


  • Add the comment as a first argument (here, cell A2)
  • Specify the list of tags as a second argument
  • Set a temperature of 1 to better capture language nuances
  • Set multi-tag as false (default true) to obtain a defined classification

Sentiment analysis GIF

Alternatively, you can classify the sentiment in a numbered scale (e.g. 1 to 5). For that, specify in the tags what the scale represents:

1=CLASSIFY_OPENAI(A2,"1 (lowest),2,3,4,5 (Highest)",false,1,500)