Abstracts multiple LLM providers (OpenAI, Anthropic, Cohere) behind a unified interface. Encapsulates provider-specific API calls, error handling, and response parsing. Low coupling with core logic (depends mainly on logger and config), making it easily extractable for any LLM application requiring multi-vendor support. Demonstrates clean dependency inversion and encapsulation of varying API contracts.

Manages evaluation of NLP tasks through a pluggable metrics system. Supports standard metrics (BERT Score, ROUGE, etc.) and custom metric registration via a simple interface. Centralizes metric computation, result aggregation, and score normalization. Minimal internal dependencies (primarily logger and external NLP libraries). Exhibits strong separation of concerns and extensibility, allowing reuse in any NLP evaluation pipeline without modification.

Provides reusable text parsing and cleaning functions (e.g., removing extra whitespace, extracting JSON from LLM outputs, normalizing case). Stateless pure functions with zero external dependencies beyond standard library. High cohesion and complete isolation from domain-specific logic. Represents a well-crafted utility module that could be dropped into any text-processing project with no changes, showcasing attention to edge cases and defensive programming.