🚀 Hamming - Let AI optimize your prompts (free for 7 days)

👋 Sumanyu and Marius from Hamming AI; we're part of the upcoming S24 batch!

TLDR: Are you spending a lot of time optimizing prompts by hand? We're launching our Prompt Optimizer (new feature in beta) to automate prompt engineering. It's completely free for 7 days!

🌟 Click here to try our Prompt Optimizer 🌟

Convert your task into an optimized prompt in minutes

Thought experiment: What if we used LLMs to optimize prompts for other LLMs?

Problem: Writing prompts by hand is tedious

Writing high-quality and performant prompts by hand requires enormous trial and error. Here's the usual workflow:

1. Write an initial prompt.
2. Measure how well it performs on a few examples in a prompt playground. Bonus points if you use an experimentation platform like Hamming to automate this flow.
3. Tweak the prompt by hand to handle cases where it's failing.
4. Repeat steps 2 & 3 until you get tired of wordsmithing.

What's worse, new model versions often break previously working prompts. Or, say you want to switch from OpenAI GPT3.5 Turbo to Llama 3. You need to re-optimize your prompts by hand. ❌

Our take: use LLMs to write optimized prompts

Describe your task, add examples, or let us synthetically create some, and click run.

Behind the scenes, we use LLMs to generate different prompt variants. Our LLM judge measures how well a particular prompt solves the task. We capture outlier examples and use them to improve the few-shot examples in the prompt. We run several "trials" to refine the prompts iteratively.

Benefits:

• No more tedious word-smithing.
• No more scoring outputs by hand.
• No need to remember to tip your LLM or ask it to think carefully step-by-step.

Meet the team

Sumanyu previously helped Citizen (a safety app; backed by Founders Fund, Sequoia, 8VC) grow its users by 4X and grew an AI-powered sales program to $100s of millions in revenue/year at Tesla.

Marius previously ran data infrastructure @ Anduril, drove user growth at Citizen with Sumanyu, and was a founding engineer @ Spell (an MLOps startup acquired by Reddit).

Our ask

In this launch, we showed how we help teams optimize each prompt. In our next launch, we'll walk through how teams use Hamming to optimize their entire AI app.

• Feedback. We want you to throw real-world tasks at our optimizer and tell us what's working and where we can improve.
• Warm intros. We'd love intros to anyone you know who writes a lot of prompts by hand or cares about solving AI reliability (including you!)

Email us here.

Book time on our calendly.