Anu Jagga Narang

Overview

Anu Jagga Narang is a product leader and commentator focused on practical AI product management, especially where strategy, evaluation, implementation, and day-to-day workflows intersect. Across newsletter mentions, she consistently emphasizes that successful AI work is not just about having a strong vision or adopting the latest tools—it depends on defining purpose clearly, grounding decisions in company-specific context, and building systems that can be implemented in real teams.

For AI Product Managers, her perspective matters because it connects several core challenges: evaluating AI quality with rubrics, understanding real product impact beyond headline metrics, adapting operating models as AI blurs traditional role boundaries, and managing context across multiple LLM tools. Her examples are especially useful for PMs looking for lightweight, non-technical ways to operationalize AI workflows, such as using an Obsidian vault as shared context for Claude Code and ChatGPT.

Key Developments

• 2026-01-31: Anu Jagga Narang wrote about why product transformation initiatives stall even when the end vision is clear, arguing that teams need supportive organizational context rather than generic encouragement to "be braver."
• 2026-03-08: She described how AI is eroding traditional boundaries between PMs, developers, and designers by enabling PMs to prototype earlier, developers to draft user stories directly, and designers to ship working variations faster.
• 2026-04-01: She shared an approach to eval rubrics that tracks accuracy, hallucinations, and toxicity across customer conversations, while stressing that evals do not replace core product thinking about purpose, audience, and success criteria.
• 2026-04-08: She distinguished between expected benefits and actual impact, noting that a feature can improve north-star metrics while still creating downstream harm for support teams or adjacent functions.
• 2026-08-08: In remarks recapped from ITX, she argued that AI strategy depends on four company-specific factors: the customer problem, the technology, cost constraints, and the definition of success. She also urged teams to question whose context any playbook reflects.
• 2026-10-06: She shared a practical workflow for setting up an Obsidian vault connected to multiple LLMs, including Claude Code and ChatGPT, with interchangeable builder and reviewer roles. She framed Markdown-based context management as an accessible option for non-technical PMs.

Relevance to AI PMs

1. She offers practical multi-LLM workflow patterns. AI PMs can apply her Obsidian-based setup to maintain shared context across tools, reduce prompt repetition, and assign different LLMs distinct roles such as drafting, critiquing, or reviewing.

2. She sharpens how PMs evaluate AI products. Her emphasis on eval rubrics for accuracy, hallucinations, and toxicity gives PMs a concrete starting point for monitoring model quality, while her broader framing reminds them to anchor evals in product purpose and user outcomes.

3. She pushes PMs to measure implementation reality, not just strategic intent. Her distinction between benefits and impact helps AI PMs look beyond top-line metrics to second-order effects on support, operations, and cross-functional teams.

Related

• eval-rubrics: Closely tied to her view that AI products need structured measurement for quality dimensions like accuracy, hallucinations, and toxicity.
• pms: Much of her guidance is aimed at product managers adapting their workflows, decision-making, and scope in AI-native environments.
• developers and designers: She highlights how AI reduces handoff friction and blurs traditional boundaries across these roles.
• product-transformation: A recurring theme in her work, especially around why transformation efforts fail without the right organizational conditions.
• itx: Referenced as the setting for her remarks on company-specific AI strategy and implementation context.
• obsidian: Central to her practical context-management workflow for PMs using Markdown as lightweight infrastructure.
• claude-code and chatgpt: The two LLM tools she explicitly connects to the same vault and uses in interchangeable builder/reviewer roles.