AI Product Strategy
The SEO Risk Of AI-Generated Content Nobody Mentions
A practical look at AI-generated content SEO risk, including sameness, weak ownership, factual drift, cannibalization, and governance gaps.
AI Product Strategy Insights
Redstone Foundry insights on AI Product Strategy, with practical notes for technical direction, implementation risk, and durable web systems.
Category Snapshot
18 insights
Latest published:
Coverage: Apr 2026 to Sep 2026
92 min total read
AI Product Strategy
How To Tell If Your AI Feature Is Actually Working
A practical way to measure AI feature success with useful product metrics, review loops, cost signals, and risk-aware quality checks.
AI Product Strategy
Embeddings 101 For Product Teams
A plain-language primer on embeddings for product teams, including what they are, where they help, where they fail, and how to plan implementation.
AI Product Strategy
Prompt Engineering As An Engineering Discipline
A practical guide to production prompt engineering, including prompt specs, versioning, evals, context design, ownership, and release discipline.
AI Product Strategy
AI Search Inside Your Documentation
A practical guide to AI documentation search, including content preparation, retrieval design, answer UX, permissions, freshness, and success metrics.
AI Product Strategy
Build, Buy, Or Borrow: AI Feature Decisions
A practical framework for deciding whether to build, buy, or borrow AI capabilities based on differentiation, risk, cost, speed, and control.
AI Product Strategy
How To Run AI Evals Without A Research Team
A practical guide to LLM evals for small teams, including example sets, rubrics, manual review, automated checks, regressions, and launch readiness.
AI Product Strategy
Why Reviewable AI Workflows Beat Magic
A practical guide to human-in-the-loop AI patterns, including review points, approvals, audit trails, risk tradeoffs, and user trust.
AI Product Strategy
AI Chatbots That Do Not Embarrass The Brand
A practical guide to brand-safe AI chatbot design, including scope, source control, tone, escalation, failure states, and launch review.
AI Product Strategy
Claude Versus OpenAI: How To Pick Without Religion
A vendor-neutral framework for choosing between Claude and OpenAI for product features, based on workflow fit, quality, cost, latency, risk, and operations.
AI Product Strategy
Internal Copilots: The Quiet AI Wins
A practical look at internal AI copilots, where they create business value, and how to start with workflows that reduce friction without adding risk.
AI Product Strategy
Vector Search For Marketing Sites: Is It Worth It
A practical guide to deciding whether vector search belongs on a marketing website, where it helps, and when simpler search is the better choice.
AI Product Strategy
RAG In Production: Where It Wins And Where It Breaks
A practical production guide to retrieval augmented generation, including where RAG is useful, where it fails, and how to design for trust.
AI Product Strategy
How To Cost A Claude Or GPT Integration Before Building It
A practical guide to estimating LLM API cost before build, including token usage, model tiers, retries, retrieval, evals, logging, and operating assumptions.
AI Product Strategy
Designing Guardrails For LLM Features In Production
A practical guide to LLM guardrails for production products, including data boundaries, tool permissions, review paths, testing, and ownership.
AI Product Strategy
The Smallest Useful AI Feature Worth Shipping
A focused framework for defining a minimum viable AI feature that is useful, reviewable, measurable, and safe enough for production.
AI Product Strategy
When To Add AI To A Product, And When To Wait
A practical decision guide for product teams deciding whether an AI feature belongs in the roadmap now, later, or not at all.
AI Product Strategy
AI Features That Earn Their Place In A Product
A practical framework for deciding when AI belongs in a product, where it adds value, and how to design reviewable workflows users can trust.
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