Empower tech and non-technical teams to test your LLM apps and ship reliable products faster.
Ottic connects with the tools your QA and engineers uses everyday, right out of the box.
Get rid of hardcoded prompts. Create, manage, and track prompts effortlessly. Bridge the gap between technical and non-technical team members, ensuring seamless collaboration in prompt engineering.
Cover any real-world scenario and build a comprehensive test suite. Break-down test cases into granular test steps and detect regressions in your LLM product.
Run tests by sampling and optimize your budget. Drill down on what went wrong to produce more reliable LLM apps.
Gain direct visibility into how users interact with your app in real time. Add chat sessions to your dataset and enhance your test suite.
We help you scale QA for LLMs
across your organization.
Boost productivity through direct access to support and expertise.
Simplify access and enhance security with single sign-on capabilities.
Collaborate securely with granular permissions for users.
Ottic connects with the tools your QA and Engineers uses everyday, right out of the box.
Software testing is usually straightforward, following established methods. But when it comes to testing Language Model (LLM) apps, things get tricky. Unlike regular software, which we can check with standard methods and tools, testing LLM apps brings new challenges and complexities to which most teams are ill-prepared to navigate. When it comes to evaluations and testing applications built on top of LLM, the traditional software QA playbook and tools fall short. There are lot of reasons for
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If you build (and don’t test), they will come (and churn)! There are many reasons why you should be serious about building a test strategy for your LLM app: * If you don’t find bugs, your customers or users will. * Post-release debugging is the most expensive form of development. * Buggy software hurts operations, sales, and reputation. There are various testing approaches available. Here are 3 principles for building a solid testing strategy for your LLM app: 1) Make it risk-focused.
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It has never been easier to build on top of a LLM. But it has never been HARDER to conduct Quality Assurance (QA), measure performance, and actually IMPROVE what you’ve built on top of it. In 2023, we've seen a proliferation of Large Language Model (LLM) releases. With each bragging superiority of their performance, makes it harder to filter true progress made in the field and which is the current winner by impressive claims about their performance. Not to mention some other highlights not inc
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