Converse with ChatGPT at Scale Using Clay
Looking to get more from your Clay tables by using artificial intelligence? This guide explains how to use ChatGPT in Clay, offering a detailed look at the key differences between GPT-3 and Chat GPT and steps on how to integrate them into your table. The goal? To provide more context, get more customized output, and increase cost-effectiveness.
Step 1: Understand the Difference Between GPT-3 and Chat GPT
OpenAI's GPT-3 has been a go-to feature for data interpretation. It can take substantial amounts of data and create text based on it. The exciting part about Chat GPT is that you can give it more context compared to the regular GPT-3 prompt. This context is particularly useful when writing emails, normalizing data, or crafting opening lines, enabling the AI to generate text exactly how you want it.
Step 2: Set Up Chat GPT in Clay
Setting up Chat GPT in Clay is straightforward. Click on 'enriched data,' then either click on 'AI GPT-3' or 'providers.' This will take you to the OpenAI integration. Click on 'Converse with Chat GPT,' and you'll see the input prompts.
Step 3: Decide on the Number of Messages
You'll need to decide how many messages you want to exchange with the AI. Think about the usual Chat GPT interface where you input some text, and it provides an output based on previous conversation context. You can choose to train the model with two messages and get an output or train it with five messages and then get an output. It's entirely up to you.
Step 4: Assign Roles to Messages
You need to assign a role to each of the messages. 'System' is the context you're providing for the AI. For instance, you could tell the system that it's a translator specializing in translating English into German, providing it the context of the role it should play.
'User' refers to your interaction with Chat GPT, i.e., the inputs you're typing in.
'Assistant' refers to the outputs generated by Chat GPT. If you want to train the AI to provide a specific output when it sees a certain input, you can do so using the 'assistant' role.
Step 5: Train the Model with Examples
Provide several examples to train the model adequately. If you're trying to normalize job titles from LinkedIn, for instance, you can give the AI a series of messages as input and tell it how you want the output to be for each case.
Step 6: Generate Your Result
When you're ready to generate your result, give it the final input from the Clay table. Save the changes and hit play. The AI will respond based on the training you provided.
Step 7: Use Chat GPT for Different Tasks
You can use Chat GPT for tasks that you previously used GPT-3 for, like crafting the first line of a cold email. It's cheaper and can produce more personalized results due to the extra context you can provide.
This is just the beginning of how you can use Chat GPT within Clay. Keep experimenting with different workflows and see how it can boost your processes. For more information, reach out on LinkedIn or join our Slack community. Thanks for reading!