Intelligence Prompting

Reviewed topic hub

Generative AI systems: models, RAG, evaluation, cost, and deployment

A production generative AI system is a chain of decisions: model, context, retrieval, tools, output controls, evaluation, and monitoring. This hub turns those choices into practical design criteria for teams and builders.

  • Model and architecture selection
  • Retrieval-augmented generation
  • Quality, latency, and cost evaluation
  • Security, privacy, and change management

Learning path

Build understanding in the right order.

Start with the system model, then move into design, evaluation, and production controls. Each article links concepts to decisions.

01

24 min read

How to Get Traffic From AI Search in 2026: A Practical GEO Playbook

Learn how to earn citations and referral traffic from ChatGPT Search, Google AI Overviews, AI Mode, Bing Copilot, and other answer engines.

  • AI search visibility starts with ordinary technical SEO: the page must be accessible, indexable, internally linked, and eligible to appear with a useful snippet.
  • Citation-ready content gives a direct answer, defines terms, supports claims with primary evidence, adds original value, and separates facts from interpretation.
  • Measure citations, AI referral sessions, engagement, assisted conversions, and query coverage—not rankings alone—and improve pages from observed retrieval gaps.
Read the field guide
02

13 min read

RAG system design: retrieval that improves answers

Design source ingestion, chunking, retrieval, reranking, citations, evaluation, and freshness for grounded AI responses.

  • RAG quality depends on source and retrieval quality before generation.
  • Chunking must preserve the meaning needed by the question.
  • Evaluate retrieval and answer grounding separately.
Read the field guide