We&rsquo;re excited to announce that <a href="https://scale.com/spellbook">Spellbook</a> is now fully available for self-serve signups!&nbsp;
Spellbook is Scale&rsquo;s platform for large language model (LLM) apps. We believe the future of data science and machine learning is people building lightweight applications on top of large language models.&nbsp;
Speed and infrastructure quality is key&ndash;how easy is it to create a new LLM app, how fast can teams experiment, and how quickly can they get feedback on whether that experiment is better than a baseline or not? Once those experiments have signs of life, how efficiently can teams fine tune them, hone them with expert feedback, and launch them into production?&nbsp;
I&rsquo;ll walk through how you can create your own production-ready LLM app in less than 10 minutes. Follow along at <a href="https://spellbook.scale.com">https://spellbook.scale.com</a>!&nbsp;
App Creation
As an easy example, let&rsquo;s get started by creating a new marketing copy generation app that will take an event, size, and demographic, and generate an event description.&nbsp;
<figure class="image align-center"><img title="test" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/c23f338d-0dd8-492a-a458-70ff95c024fa" width="624" height="280">
<figcaption>App setup page in Spellbook</figcaption>
</figure>
<figure class="image"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/2acb3b28-73c0-4bea-8ad3-385a5aba254a" width="580" height="423">
<figcaption>Example dataset with event, size, and demographic data.</figcaption>
</figure>
Quickly set up your prompt and see how your prompt runs on multiple rows of data.&nbsp;
<img src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/35ca21e5-3b43-4feb-ba8e-dd6d5215c8cb" width="3159" height="1599">
Evaluate Your LLM App
Make sure your model is outputting good generation with our human evaluation pipeline. Define evaluation criteria on how a human should determine whether your model output is good or bad. Here, we want to ensure our event description is relevant.
<figure class="image"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/6ab0f3da-e7c8-4258-af87-b4455f71a35b" width="624" height="317">
<figcaption>Define human evaluation criteria for your model output.</figcaption>
</figure>
You&rsquo;ll get your results back in the form of a &ldquo;Hit rate&rdquo; along with downloadable results you can use to fine tune your model!
<figure class="image"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/d9584b8a-0898-41a9-ac5c-70c343a7faff" width="624" height="319">
<figcaption>Receive evaluation results back after humans have finished reviewing.</figcaption>
</figure>
Deploying Your App
Once you have a model you&rsquo;re satisfied with, you can deploy your model to production in one click through our deployments support.&nbsp;
<figure class="image"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/6d34a534-3432-4925-a3de-51dfef56ef71" width="624" height="315">
<figcaption>Create a new deployment for an App Variant.</figcaption>
</figure>
You&rsquo;ll receive an API endpoint that you can integrate directly into production, with built-in monitoring!&nbsp;
<figure class="image"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/e8a2dfe8-b11c-4ecf-9d2d-30bbcc04ae57" width="624" height="315">
<figcaption>Integrate your LLM app into production using our code snippets, and monitor your requests and latency.</figcaption>
</figure>
As you improve your LLM apps, you can keep swapping them out in your deployments tab by selecting the most preferable variant. This ensures that once you&rsquo;ve gotten an endpoint integrated, you don&rsquo;t have to update your code again.&nbsp;
<figure class="image"><img src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/ded46a41-1762-41f0-b7a2-732f2f2e420c" width="624" height="335">
<figcaption>Swap out your deployments with updated variants as you improve the quality of your LLM app.</figcaption>
</figure>
<h4 dir="ltr">Try Spellbook</h4>
As LLMs become more powerful, the ability to quickly deploy enterprise quality LLM apps to production and quickly iterate based on user feedback is crucial. Try <a href="http://spellbook.scale.com">Spellbook</a> today to create your first apps!&nbsp;
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We believe the future of data science and machine learning is people building lightweight applications on top of large language models. 