Shivam.Consulting Blog
AI products, enablement & hiring
- AI Strategy (274)
- Generative AI (105)
- IT Leadership (32)
- Leadership (78)
- Product Management (324)
- Product Management Leadership (286)
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How to Roll Out AI Without Dodging the Job Security Question
A practical playbook for making credible job commitments, testing AI on business outcomes, and showing employees how their roles will change.
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Persistent Memory for AI Agents: A Product Leader’s Guide
A practical framework for deciding what an AI agent should remember, how it should retrieve and forget memories, and how to test the value safely.
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Chinese Frontier AI Models: A Product Leader’s Decision Guide
A practical framework for choosing Kimi K3, Qwen 3.8 Max, or DeepSeek V4 Flash by completion reliability, accepted-result cost, and product fit.
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Context Engineering: How to Build Reliable AI Applications
A practical framework for designing context, diagnosing agent failures, budgeting each turn, and evaluating AI applications before production.
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SpaceX Orbital AI: How to Evaluate the Revenue Case
A practical framework for separating SpaceX’s reported AI revenue from the technical and economic proof required to scale orbital compute.
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How to Design Persistent Memory for Reliable AI Agents
A practical framework for deciding what an AI agent should remember, retrieving it safely, correcting stale facts, and proving that memory helps.
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Governing AI Security Beyond the Open-Weights Debate
A practical operating model for product leaders to govern jailbreak disclosure, open-weight releases, agent containment, and incident-response readiness.
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How to Give Your AI Strategy Financial Staying Power
A practical framework for sequencing AI investment, testing unit economics, preserving optionality, and surviving timelines you cannot control.
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Hidden Assumptions in Product Discovery: A Practical Test
A practical method to expose, rank, and test the desirability, viability, feasibility, usability, and ethical assumptions behind product ideas.
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AI Builder Maturity: From Fast Demo to Defensible Product
Use a five-level evidence ladder to diagnose your AI product, test platform risk, and invest in advantages that strengthen as models improve.
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AI Agent Skill Overload: How to Curate What Stays
Learn how to test, approve, fork, and retire AI agent skills before instruction conflicts and hidden assumptions weaken your team’s output.
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How Product Leaders Should Govern Open and Closed Frontier AI
A practical framework for choosing open or closed frontier models, limiting agent blast radius, and building security brakes your team can test.
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Embodied AI: A Product Leader’s Guide to Generalist Robotics
Use a task-first framework to choose embodied AI workflows, measure useful autonomy, design safety boundaries, and make a sound robotics investment.
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The July 2026 AI Evaluation Lab Leak: A Leader’s Playbook
A practical governance and containment playbook for testing dangerous model capabilities without turning an internal evaluation into a real-world incident.
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Visual Search for Messy B2B Catalogs: An Intent-First Playbook
A practical framework for turning ambiguous product images into intent-aware retrieval, trustworthy ranking, and measurable B2B sourcing outcomes.
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How to Evaluate AI Risks That Emerge After Deployment
A practical framework for testing stateful AI across long user trajectories, monitoring drift after launch, and resisting misleading satisfaction metrics.
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Persistent AI Agent Memory: A Knowledge Graph Blueprint
A practical blueprint for modeling, governing, retrieving, and evaluating knowledge graph memory that an AI agent can safely use over time.
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How to Build Persistent Business Memory with AI Knowledge Graphs
Learn how to design a time-aware, evidence-linked knowledge graph that lets AI recover current decisions, their history, and provenance.
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Codex Token Cost Optimization Without Losing Useful Context
A practical model for trimming Codex context, preserving decisions, and proving token savings without creating more retries or review work.
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A Practical Guide to Metaphors for Understanding Language Models
A practical framework for choosing AI metaphors, exposing their hidden assumptions, and translating human-sounding behavior into testable product claims.
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Browse topics
- AI Strategy (274)
- Generative AI (105)
- IT Leadership (32)
- Leadership (78)
- Product Management (324)
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