Autoblocks AI

Automatically evaluates LLM-generated outputs by comparing semantic similarity between expected and actual responses. Uses advanced NLP techniques to assess whether outputs are functionally equivalent even if not identical.

Identifies and flags instances where LLM outputs contain factually incorrect, fabricated, or unsupported information. Analyzes responses against knowledge bases or source documents to detect hallucinations.

Automatically detects performance degradation or quality regressions when deploying new versions of LLM applications. Compares metrics and test results between versions to identify issues before production impact.

Allows developers to define and build custom test suites tailored to their specific LLM application requirements. Supports multiple evaluation metrics and assertion types beyond standard benchmarks.

Captures and monitors LLM prompts and responses in production, tracking performance metrics like latency, token usage, and cost. Provides real-time visibility into how prompts perform in live environments.

Provides a centralized dashboard displaying key performance indicators and metrics for LLM applications in production. Visualizes latency, cost, error rates, and custom metrics developers need to track.

Integrates with popular LLM APIs (OpenAI, Claude, etc.) through lightweight SDKs that require minimal changes to existing code. Allows teams to add monitoring and testing without major architectural changes.

Enables testing of multiple prompts and variations in batch mode, evaluating them against test suites and metrics. Useful for comparing prompt performance at scale and identifying optimal variations.

Provides tools to investigate and understand why LLM outputs failed tests or produced unexpected results. Captures detailed context about prompts, parameters, and responses to aid debugging.

Reduces the time between code changes and validation by providing immediate test results and feedback. Enables developers to iterate quickly on prompts and LLM configurations.

Monitors and tracks costs associated with LLM API calls, token usage, and model selection. Identifies opportunities to optimize spending through prompt efficiency or model selection.