How to use OpenAI's ChatGPT for keyword research
Learn how to use OpenAI and GPT-3 inside the spreadsheet to generate keywords, translate them, find common misspellings and more.
How to use OpenAI in Rows
Connecting the OpenAI integration in Rows
You can find the OpenAI integration by browsing the integrations gallery and searching for "OpenAI".
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.
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.
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,
or via the Actions wizard:
All OpenAI functions need to be configured through mandatory and optional parameters, depending on their purpose. Let's go through them one by one.
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")
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 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 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 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:
|LATEST MODEL||DESCRIPTION||MAX TOKENS||TRAINING DATA|
|gpt-3.5-turbo||Most 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 tokens||Up to Sep 2021|
|gpt-3.5-turbo-0301||Snapshot 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 tokens||Up to Sep 2021|
|text-davinci-003||Can 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 tokens||Up to Jun 2021|
|text-davinci-002||Similar capabilities to text-davinci-003 but trained with supervised fine-tuning instead of reinforcement learning||4,097 tokens||Up to Jun 2021|
Number of items (optional)
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)
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.
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
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 OpenAI functions for keyword research
There are several ways to use OpenAI to automate keyword research tasks inside a spreadsheet:
- Generate keywords: Generate keyword ideas based on a topic.
- Generate variations of keywords: Generate new keywords based on a list of keywords.
- Generate common misspellings of keywords: Generate keywords that represent the common typing errors users do when looking for your target keywords.
- Translate keywords: Translate a list of keywords to several languages to capture traffic in multiple geographies.
Let's go through each of them.
Goal: Generate keyword ideas based on a topic. Create separate lists for Informational, Transactional, Navigational and Commercial keywords.
1=ASK_OPENAI(CONCATENATE("Generate a list of 10 Informational keywords based on the topic of: ",A2))
Add new columns to the spreadsheet for different keyword types.
Details: Assumes that A2 contains the topic.
💡 To have the list of keywords in a new table, use the CREATE_LIST_OPENAI() function from the Actions menu. Input the prompt 'Generate a list of Informational keywords based on the topic of Vegan dog food'.
Then specify the number of items you expect, here 10. Set a temperature of 1, to allow for creative responses, and max_tokens of 500.
Once the function has been configured, click on Next step and your table will appear below.
Generate variations of keywords
Goal: Generate new keywords based on a list of keywords.
There are two steps to generate a variation of keywords:
- Creating a list with the keywords that will be used as a source.
- Using OpenAI to generate a new list of keywords.
RANGE2JSON to create a list with the first group of keywords. We'll use this formula inside the ASK_OPENAI
prompt to generate the new list.
With the list of source keywords, we'll use OpenAI to generate the variations. We'll use the
=RANGE2JSON(A1,A2:A) inside the
prompt of the
1=ASK_OPENAI(CONCATENATE("List new keywords based on this list: ",RANGE2JSON(A1,A2:A)),10,,)
💡 Add a new table in the spreadsheet and move the OpenAI formula to keep the table with the keywords easier to read and use.
Generate common misspellings of keywords
Generate a list of common mispellings of given keywords.
1=ASK_OPENAI(CONCATENATE("Generate a json list with 1 column with the most common misspellings of ",B1),,500)
💡 Tips on improving the results
- Set a value for the
max_tokenparameter to ensure it returns the full list.
- Be specific on the number of columns you expect. By default, OpenAI will return a column with misspellings and another with the correction.
Assumes that the source keyword is in B1.
Goal: Translate a list of keywords to several languages to capture traffic in multiple geographies.
Add new columns with the languages you want to translate to. In this example, we added additional columns for Japanese and Arabic.
- Assumes that A2 contains the keyword.
- Set a temperature of 1, to account for nuances and idiomatic expressions
- Set max_tokens high enough to translate longer sentences.
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