The Challenge

As Anterior began building LLM-native applications, they encountered new challenges in software development that traditional testing paradigms couldn't address. Some key pain points included:

Unpredictable outputs. The inherent randomness of large language models meant that functionality could work one day and fail the next, without any software updates.
Lack of established testing frameworks. The step change in software development brought on by LLMs left a gap in reliable testing methods for these new applications.
Resource constraints. As an early-stage startup, Anterior needed to keep their engineering team lean and focused, making it impractical to build their own evaluation framework from scratch.

The Solution

After evaluating several options, Anterior chose Autoblocks for its unopinionated approach and seamless integration capabilities. Autoblocks provided:

Easy SDK integration. Developers could easily incorporate Autoblocks into their existing codebase and testing workflows.
Comprehensive UI. A user-friendly interface allowed both technical and non-technical team members to view evaluation outputs, prompts, and model responses.
Adaptability. The unopinionated nature of Autoblocks allowed Anterior to evolve their evaluation strategy alongside rapidly changing LLM technologies.

Autoblocks quickly became an integral part of Anterior's development process, enabling them to improve the reliability, consistency, and accuracy of their LLM-based products.

The Impact

Implementing Autoblocks had several positive effects on Anterior's operations:

Increased product velocity. By seamlessly integrating evaluations into the development workflow, Autoblocks removed barriers to shipping, allowing the team to move faster.
Enhanced developer confidence. AI engineers gained assurance in their code changes, knowing that Autoblocks was constantly monitoring for potential issues.
Improved cross-team collaboration. The easily shareable evaluation results facilitated communication between engineers, product managers, and domain experts.
Cost savings. Early detection of issues through Autoblocks' evaluations helped Anterior avoid expensive errors when running models at scale.
Future-proofing. The flexibility of Autoblocks positioned Anterior to quickly adapt to new model releases and changing evaluation metrics in the fast-paced AI landscape.

By leveraging Autoblocks, Anterior was able to maintain a high-performance engineering team that ships frequently while ensuring the reliability and quality of their AI-powered products in the healthcare sector.

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