data & learning flywheel for LLM systems
APACHE-2.0 License
TensorZero enables LLM applications that learn from real-world experience.
It provides a data & learning flywheel for LLMs by unifying:
Our goal is to help engineers build, manage, and optimize the next generation of LLM applications: systems that learn from real-world experience. Read more about our Vision & Roadmap.
Next steps? The Quick Start shows it's easy to set up an LLM application with TensorZero. If you want to dive deeper, the Tutorial teaches how to build a simple chatbot, an email copilot, a weather RAG system, and a structured data extraction pipeline.
Questions? Ask us on Slack or Discord.
Using TensorZero at work? Email us at [email protected] to set up a Slack or Teams channel with your team (free).
We are working on a series of complete runnable examples illustrating TensorZero's data & learning flywheel.
Writing Haikus to Satisfy a Judge with Hidden Preferences
This example fine-tunes GPT-4o Mini to generate haikus tailored to a specific taste. You'll see TensorZero's "data flywheel in a box" in action: better variants leads to better data, and better data leads to better variants. You'll see progress by fine-tuning the LLM multiple times.
Improving Data Extraction (NER) by Fine-Tuning a Llama 3 Model
This example shows that an optimized Llama 3.1 8B model can be trained to outperform GPT-4o on a Named Entity Recognition (NER) task using a small amount of training data, and served by Fireworks at a fraction of the cost and latency.
Improving LLM Chess Ability with Best-of-N Sampling
This example showcases how best-of-N sampling can significantly enhance an LLM's chess-playing abilities by selecting the most promising moves from multiple generated options.
Improving Data Extraction (NER) with Dynamic In-Context Learning
This example demonstrates how Dynamic In-Context Learning (DICL) can enhance Named Entity Recognition (NER) performance by leveraging relevant historical examples to improve data extraction accuracy and consistency without having to fine-tune a model.
Improving Math Reasoning with a Custom Recipe for Automated Prompt Engineering (DSPy)
TensorZero provides a number of pre-built optimization recipes covering common LLM engineering workflows. But you can also easily create your own recipes and workflows! This example shows how to optimize a TensorZero function using an arbitrary tool — here, DSPy.
& many more on the way!