nlp-engineering

What problem does it solve?

This skill provides a practical framework for building production NLP systems, guiding engineers from raw text ingestion through tokenization, embeddings, retrieval, classification, and generation, with an emphasis on robust architecture and maintainability.

Core Features & Use Cases

• End-to-end NLP pipeline guidance spanning preprocessing, embedding, retrieval, classification, and summarization.
• Best-practice decision guidance on model selection, chunking, evaluation, and deployment strategies.
• Real-world workflows such as semantic search, RAG, text classification, and named-entity extraction in production environments.

Quick Start

Outline a starter NLP pipeline with preprocessing, embeddings, and a basic retrieval setup for a sample text corpus.