Notysoty

Reviews existing unit tests for gaps, weak assertions, and missing edge cases, then rewrites them to be more robust.

Given a document type and retrieval goal, recommends the optimal chunking strategy for a RAG pipeline to minimize retrieval failures.

Rewrites landing page copy to be more compelling, benefit-focused, and conversion-optimized.

Converts a blog post, idea, or document into an engaging Twitter/X or LinkedIn thread with hooks and CTAs.

Diagnoses why tests pass inconsistently and suggests fixes for timing, ordering, and state isolation issues.

Writes a blameless incident postmortem document from an incident timeline or Slack thread.

Designs a complete Retrieval-Augmented Generation (RAG) pipeline for a given use case, including chunking strategy, embedding model selection, and retrieval approach.

Reviews web forms for usability issues — field ordering, validation messages, error states, and accessibility.

Generates clear, user-friendly release notes from a git log or list of PRs.

Reviews database schemas for normalization issues, missing indexes, naming inconsistencies, and scalability risks.

Designs a hybrid retrieval pipeline combining dense vector search and BM25 sparse search with reciprocal rank fusion, and explains when to use each configuration.

Writes a complete product spec for an AI-powered feature including user stories, model requirements, fallback behavior, and evaluation criteria.