Which language model works best your prompt? Compare your text generations across multiple large language models (LLMs) like OpenAI's evolving and latest ChatGPT engines and others like Curie, Ada, Babbage.
Run
Examples
API
Functions give your workflow the ability run Javascript code (with webcalls!) allowing it execute logic, use common JS libraries or make external API calls before or after the workflow runs. Learn more.
After
Example POST Request
Add Function
Variables let you pass custom parameters to your workflow. Access a variable in your instruction prompt with Jinja, e.g. {{ my_variable }} Learn more.
{{ my_variable }}
messages
Add
⚙️ Settings
Avoid Repetition
JSON Object
The maximum number of tokens to generate in the completion. Increase to generate longer responses.
Higher values allow the LLM to take more risks. Try values larger than 1 for more creative applications or 0 to ensure that LLM gives the same answer when given the same user input.
How many answers should the copilot generate? Additional answer outputs increase the cost of each run.
Run cost = 4 credits
Breakdown: 3Cr for GPT-4 Turbo (openai) + 1Cr/run
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GPT-4 Turbo (openai)
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