gteam-ai-engineer

What problem does it solve?

Provides a pragmatic senior-engineer playbook for turning ML research and prototypes into reliable production features, reducing guesswork around model choice, retrieval, deployment, and evaluation.

Core Features & Use Cases

• Model Integration & Selection: Choose appropriate model families, implement API patterns, and design tiered routing and retry/fallback strategies.
• RAG & Retrieval: Ingestion, chunking, embedding, vector store selection, hybrid retrieval, re-ranking, and context assembly for grounded generation.
• Prompt Engineering & MLOps: Production prompt templates, structured output enforcement, deployment checklists, monitoring, A/B testing, and evaluation pipelines (LLM-as-judge, human eval).
• Use Case: Build a customer support RAG chatbot with weekly updates, sub-3s response targets, source citations, and continuous quality monitoring.

Quick Start

Design a RAG pipeline for 500 Markdown help articles, select embedding and vector store options, and produce an ingestion, chunking, retrieval, and monitoring plan.