Optimize LLM Ops and Prompt Engineering with
Weights & Biases
See why leading ML teams rely on the W&B platform to train, track, tune and manage their end-to-end LLM operations.
Trusted by the teams building state-of-the-art LLMs
Adam McCabe
Head of Data
Head of Data
“The challenge with GCP is you’re trying to parse terminal output. What I really like about Prompts is that when I get an error, I can see which step in the chain broke and why. Trying to get this out of GCP is such a pain.”
Peter Welinder
VP of Product- OpenAI
VP of Product- OpenAI
“We use W&B for pretty much all of our model training.”
Ellie Evans
Product Manager- Cohere
Product Manager- Cohere
“W&B lets us examine all of our candidate models at once. This is vital for understanding which model will work best for each customer. Reports have [also] been great for us. They allow us to seamlessly communicate nuanced technical information in a way that’s digestible for non-technical teams.”
Improve prompt engineering with visually interactive evaluation loops
W&B automatically tracks exploration branches of your prompt engineering experiments and organizes your results with visual, interactive analysis tools, helping you decide what works well and what to try next.
Organize text prompts by complexity and linguistic similarity with W&B Tables, to enable a visually interactive evaluation loop and better understand the best approach for your given problem.
Organize text prompts by complexity and linguistic similarity with W&B Tables, to enable a visually interactive evaluation loop and better understand the best approach for your given problem.
Examples
Keep track of everything with dataset and model versioning
Save, version and show every step of your LLM pipeline and the difference between prompt templates with W&B Artifacts. Incrementally track the evolution of your data over time and preserve checkpoints of your best performing models. Regulate, monitor, and save private and sensitive data with custom local embeddings and enterprise-level data access controls.
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Fine-tune LLMswith your own data
Build on top of state-of-the-art LLMs from OpenAI, Cohere, or any other language models with streamlined fine-tuning workflow support, including for Langchain visualization and debugging. Analyze edge cases, highlight regressions, and use W&B Sweeps to prune hyperparameters with your own data and deliver better results faster.
Examples
Maximize efficient usage of compute resources and infrastructure environments
Easily spot failure and waste in the same workspace with real-time model metric and system metric monitoring.
Use W&B Launch to easily send jobs into target environments for access to compute clusters, giving MLOps teams an easy lever to ensure the expensive resources they manage are being efficiently maximized for LLM training.
Visibility across a variety of different roles will allow teams to easily correlate model performance with GPU and compute resource usage.
Use W&B Launch to easily send jobs into target environments for access to compute clusters, giving MLOps teams an easy lever to ensure the expensive resources they manage are being efficiently maximized for LLM training.
Visibility across a variety of different roles will allow teams to easily correlate model performance with GPU and compute resource usage.
Learn
Collaborate seamlessly in real-time
The W&B collaborative interface and workflow is built to ensure seamless teamwork and easy sharing of results and feedback. The prompt engineer working on text generation can quickly pass the latest updates on to ML practitioners optimizing the models by using W&B Reports. Keep track of all your results and plan your next steps within one unified system of record.
Examples
See W&B in action
Introducing OrchestrAI: Building Custom Autonomous Agents with Prompt Chaining
The Art and Science of Prompt Engineering
How to Fine-Tune an LLM Part 1: Preparing a Dataset for Instruction Tuning
Training LLMs Using Reinforcement Learning From Human Feedback
Prompt Engineering LLMs with LangChain and W&B
Evaluating LLMs
Processing Data for LLMs
