The Next Generation of Text Generation
How we can help with prompt engineering in your LLM projects:
Prompt Benchmarking
With our prompt management tools, you can benchmark up to five LLMs at the same time by feeding the same prompt and comparing results.
Prompt Scoring & Refinement
Create scoring agents by chaining prompts together. Let your LLMs score and evaluate each other, making it simpler for you to understand how to improve.
In-Context Prompting
Upload different contexts in plain text for your prompts to take into consideration for their answers. Change contexts and call variables within prompts to get different answers.
The right outputs require the right prompts
Fine-tune your own prompts using roles, tones, temperature, examples, contexts and other prompting techniques to get the best outputs for your projects.
Use a system that allows you to fine tune and manage prompts, contexts, roles and even different LLMs.
Use any LLM you like
We are also agnostic to LLM and foundational model providers — we work with OpenAI, Mistral, LLaMA, and more. You can switch easily from an open source model to a closed source model to compare, and tune the prompt and the model depending on your task. Use the best models for your tasks.
Chain together different prompts to do more
You can set up multiple steps in your prompts, and each step can call variables from the previous one. This way you can multiply the effect of each LLM and prompt to do many more things and create smarter LLM agents.
Your prompts engineered to perfection, step by step:
Here's what you can do in four simple steps:
1. Connect your LLMs and upload contexts
The first step is to connect the API keys for all your LLMs to Standupcode, and upload your contexts in plain text if you require.
2. Define your steps
Define how many steps you want to develop prompts for, and how those steps connect to each other.
3. Define roles and prompts
Define how you want the LLM to view you as — what is your role, and what is your background — and then define the exact prompts for your LLMs.
4. Results and evaluation
See the results of your LLMs and evaluate them. Reiterate easily if required.
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