Eval Driven Developmentv0.1.11

A company deploys a chatbot to handle customer inquiries. The skill is used to instrument the chatbot's Python backend, create golden datasets from historical support tickets, and run evals to ensure responses are accurate, empathetic, and adhere to brand guidelines, catching regressions after prompt updates.

A research team builds an AI assistant that retrieves information from academic papers. The skill helps instrument the retrieval and generation pipeline, build datasets of query-answer pairs, and test for factual accuracy and relevance to prevent hallucinations in critical domains like healthcare or finance.

An e-commerce platform uses an AI agent to process orders and handle customer requests. The skill is applied to instrument the agent's tool calls and decision logic, create evals for output format compliance and error handling, and debug performance changes after model upgrades.

A software tool generates code snippets based on natural language descriptions. The skill instruments the LLM calls, builds datasets of code requirements and expected outputs, and runs evals to validate correctness, security, and adherence to coding standards before deployment.

Offer the eval-driven development skill as part of a platform for AI app testing and monitoring. Charge monthly fees based on usage metrics like number of evals run or dataset size, targeting teams building LLM applications who need reliable QA pipelines.

Provide expert services to companies adopting eval-driven development. Help clients instrument their Python apps, design eval strategies, and set up automated testing workflows, with revenue from project-based fees or retainer contracts.

Release the core skill and tools as open source to build community adoption. Monetize through premium features like advanced analytics, enterprise support, or integration with proprietary datasets, leveraging a freemium model.