AI ProductInsights

How to design, orchestrate and productize multi-agent AI systems: patterns, failure modes, governance and operational playbooks for product teams.

Practical, product-focused strategies to reduce AI inference and platform costs without sacrificing user value—architecture patterns, lifecycle controls and measurable guardrails for AI PMs.

A practical, product-focused framework for evaluating AI features and LLM-driven products: metrics, test types, tooling and an operational playbook for reliable launches.

Practical, product-focused guidance for embedding AI safety and alignment into the product lifecycle—risk assessment, governance, testing and operational controls for AI PMs.

A practical, product-centered framework to decide between fine-tuning, Retrieval-Augmented Generation (RAG) and prompt engineering — with trade-offs, cost/ops implications and step-by-step adoption guidance.

Practical strategies for deciding when and how to integrate large language models into products: architecture patterns, UX considerations, governance and rollout tactics.

How to design, prioritize and ship multimodal AI experiences that combine text, image, voice and video—architecture, UX patterns, trade-offs and operational guidance for product teams.

Practical, product-focused prompt engineering: how to design, test and operationalize prompts for reliable, scalable user experiences.

How to design, build and operationalize RAG-driven products: technical architecture, UX patterns, business value and launch strategies for AI PMs.

Practical guidance for selecting, designing and operating vector databases and semantic search as a product capability — architecture, UX, metrics and pitfalls for AI PMs.