ai-product-dev

What problem does it solve?

AI/ML product development often requires integrating LLMs, managing RAG pipelines, and evaluating AI models, which is time-consuming without a cohesive pattern catalog.

Core Features & Use Cases

• LLM integration patterns with practical best practices for retries, streaming, and cost control.
• RAG architectures covering embedding, vector search, reranking, and context assembly for reliable question answering.
• Agent design principles and prompt engineering templates to accelerate product delivery in AI-enabled workflows.
• Use Case: Build an end-to-end AI feature from model selection to deployment and evaluation.

Quick Start

Give me a starter blueprint to build an AI product using LLMs, RAG, embeddings, and evaluation.