Make AI Tools

make ai tools is available in open beta to all customers on paid plans as a beta feature, both product functionality and pricing may change make ai tools helps you use ai in your scenarios without the complexity no ai expertise required, no third party accounts needed what is make ai tools? make ai tools is a pre built app with modules that help you quickly handle common ai work, all without an llm account or knowledge about how to prompt the solution offers prebuilt ability to analyze sentiment, categorize text, identify language, extract information from text, standardize text, summarize text, translate text, chunk text, and custom prompts if you're at an early stage on your ai journey and curious about exploring ai in your automations, but feel a little intimidated by the complexity, then make ai tools is the solution for you key benefits no ai expertise required use advanced ai without learning prompting techniques no external accounts everything works within your existing make environment included with paid plans available at no additional cost during open beta privacy focused your data is not stored or used for ai training easy integration works seamlessly with your existing scenarios availability make ai tools is available in open beta to all customers on paid plans access it immediately through your make dashboard with no additional setup required usage limits each organization receives 10 million input tokens per month 10 million output tokens per month technical details ai model make ai tools uses gpt 4 1 nano data privacy make does not store your data and your data is not used to train the ai usage limits each organization has a monthly limit of 10 million input tokens and 10 million output tokens error handling when this limit is reached, a runtimeerror will be returned for more information about the error, refer to the types of errors in make interaction logs stored in scenario logs for 30 days (standard make behavior) modules basic ai analyze sentiment evaluates the tone of a text and determines if it's positive, neutral, or negative this is known as the sentiment text to analyze enter text to analyze the module will return whether the text is positive, neutral, or negative return a description of the sentiment? select yes or no example in this example, we will show how you can use the analyze sentiment module to evaluate feedback from a customer imagine you have received a feedback ticket or email in a customer service application you are using, such as freshdesk docid\ biienqq2nddhhpahdg3ey or zendesk docid\ ayxux muilq sdx tmpfm you can have the analyze sentiment module evaluate the tone of the text, determine if it's positive, negative, or neutral, and then you can use that information to sort your feedback to help you strategize to improve your results to build your {{scenario singular lowercase}} , your first module will get the information from your customer service application you might choose freshdesk search/list tickets or zendesk list tickets next, you can add the analyze sentiment module and map the value of your message in the first module to the text field you can also choose to receive a description of the sentiment as well after the {{scenario singular lowercase}} runs, the input bundle will be the message you received from your customer and the output bundle will show the sentiment expressed and a description request anything sends a request for {{product name}} ai tools to answer this is known as a prompt text enter a prompt for {{product name}} ai tools to answer example you can ask for simple instructions on how to perform specific tasks in this example, we have requested a quick guide of how to export data from an excel file to airtable the output provides clear and straightforward steps for completing this task categorize text analyzes text and assigns it to categories from a list you define text to categorize enter text to sort into a category categories add the categories the text can be sorted into return an explanation for the categorization? select yes or no example you can define your own categories and sort any text into those categories for further processing in this example, we have defined three categories, questions , positive feedback , and complaints , and the output identifies the category or categories the text belongs to identify language detects the language that text is written in text to identify enter text to identify the language example you many need to determine the language of a text to ensure that a request is forwarded to the proper team in this example, we have a text that was correctly identified as being written in czech extract information from text retrieves information that you specify from a text text enter text to extract information from information add items to extract from the text description describe the information to extract from the text name enter a parameter name for the extracted information type select the format to use for the output of the extracted information example you may have a text or customer feedback that has a lot of extraneous information in this example, we have identified the key information we need to extract order number , order date , customer name , and problem the result is a bundle that identifies the important points as defined standardize text adjusts the style and format of text to ensure it's consistent text to standardize enter text to standardize the format and style example the original text mixes up forms of the verbs, creating awkward directions that are difficult to read the result standardizes the text for better flow summarize text condenses longer text into a shorter summary text to summarize enter the text to create a summary example this module accepts any text as an input and outputs a bundle with a summary of the content translate text translates text from one language into another text to translate enter the text of any common language to create a translation target language enter the language to translate the text into advanced ai chunk text splits text into smaller, more manageable pieces known as chunks text to chunk enter text to split into smaller chunks the chunks are based on a number of tokens, which is how ai measures size chunk size enter the number of tokens to use in each chunk chunk overlap enter the number of tokens to use for text to overlap at the beginning and end of chunks if you don't want any text to overlap, enter 0 example it is sometimes necessary to split text into smaller bundles, as ai often has token limitations this module allows you to split your text into chunks that are more manageable this text is approximately 600 characters, which is equal to about 131 tokens with a chunk size of 50 and no overlap, the result is three bundles of text for more information on counting tokens, see the openai tokenizer