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"The last prompt you'll ever write."
Founded by Andrew Chung, Hyun Jie Jung, Junyoung Park & Toby Kim
Andrew and Jun built 10+ LLM-based products, open-sourced a prompt engineering platform, and co-authored a paper at a NeurIPS workshop last year. HyunJie worked on data analytics and optimization at Chartmetric and DevRev, and focused on growth marketing at Liner. Then Toby joined, a full-stack engineer who worked at several early stage teams, shipping 5+ products.
☕️ TL;DR
- Ape is the ultimate AI prompt engineer 🐒, designed to optimize your prompts by reducing cost and latency while increasing performance.
- Ape achieves an impressive 94.5% on the GSM8K benchmark, surpassing Vanilla (54.5%), CoT (87.5%) and DSPy (90.0%).
- Easy to set up evaluation: Ape can auto-generate evaluation code and use LLMs as a judge, or you can use your own eval metrics.
- Get set up in less than 15 minutes and see the difference.
- Schedule a meeting to discover more.
🔒 Problem
You’re an engineer of an LLM app, trying to get the prompts just right. Every time you type something in, the output changes—so you tweak a word here and there, and it changes again. Sometimes the outputs looks better, sometimes not. But you’re never sure. Hours go by, all spent on prompt engineering.
Getting the outputs you want can feel like an endless game of trial and error. And you’re not alone. Over the past few weeks, they’ve talked to over 100 YC companies, and a lot of them are facing the same challenges:
- Measuring output quality is hard (You’re heavily relying on manual evaluations at the moment.)
- Prompt engineering does not work as you want (You hate spending 5-7 hours a day searching for that one great prompt.)
🔑 Solution
Weavel solves the problem with one simple formula:
good input + right guidance = better prompts
Your first AI Prompt Engineer. Inspired by DSPy, Reflexion, Expel and other research papers, Ape iteratively improves your prompts. Here’s how Ape works:
1️⃣Log your inputs and outputs to Weavel (with a single line of code!)
2️⃣ Let Ape filter the logs into datasets.
3️⃣ Ape then generates evaluation code and uses LLMs as judges for complex tasks.
4️⃣ As more production data is added, Ape continues refining and improving prompt performance.
How to use
Create a Dataset
Change just one line of code to start logging LLM calls with the Weavel Python SDK. The SDK supports sync/async OpenAI chat completions and OpenAI structured outputs.
You can also import existing data or manually create a dataset.
Create a Prompt
Write a prompt that corresponds to your dataset. You can add an existing prompt as the base version, or if you prefer, create a blank prompt and provide a brief description for Ape to create a prompt from scratch.
Optimize Prompts
To optimize your prompt using Ape, fill in the necessary information (e.g. JSON schema as you want) and then run the optimization process. An enhanced version of your prompt will be created and available soon.
Ta-da! It’s that easy. Ape outperforms with a remarkable 94.5% score on the GSM8K benchmark, surpassing Vanilla (54.5%), CoT (87.5%) and DSPy (90.0%). With Ape, you can optimize the prompt engineering process, saving tons of time and cost while increasing performance.
Ape is open source.
🙏 Ask
- If you know anyone struggling with prompt engineering or evaluations for LLM apps, connect them!
- Copy & paste blurb: A YC company named Weavel has developed an AI prompt engineer (Ape in short) which continuously improves your prompts. It’ll save tons of time for you. You can grab a time here for a demo from the founders.
Learn More
🌐 Visit weavel.ai to learn more.
👉 Try Ape! Schedule a walkthrough with the Weavel team or email them here.
🤖 Share thoughts on their Discord or DM them on Twitter.
🌟 Check out their repository on GitHub. Give them a 🌟!
👣 Follow Weavel on LinkedIn & X
