Product Marketer at Tetriz
Prashasti Abojwar
Prashasti is a product marketer at Tetriz focused on AI product analysis, product storytelling, and clear explanations of technical workflows.
Her work examines how product promises, user context, and go-to-market choices shape whether AI products earn trust in practice.
Areas of expertise
- Product marketing
- AI product analysis
- Product storytelling
- Go-to-market communication
Experience and credentials
- Indian Institute of Technology Bombay
Authorship and accountability
Articles credited to Prashasti Abojwar connect the work with this profile, including their role, relevant experience, and external identity.
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Articles by Prashasti Abojwar
Are your agile metrics hiding a burnout problem?
Agile metrics built around velocity and story points are being gamed by AI coding tools that inflate estimates and accelerate task completion without improving shipped outcomes. The reliable signals now are cycle time, deployment frequency, code review velocity, and deploy-to-fix latency.
Cycle time vs lead time in AI-augmented engineering teams
Cycle time tracks how quickly code moves from commit to production (typically 2 to 7 days). Lead time tracks how quickly a feature moves from request to production (typically 2 to 8 weeks). AI tools reduce the first but rarely touch the second, and the widening gap between them reveals where your real bottleneck sits.
Why lines of code fails as a developer productivity metric
Developer productivity metrics built around lines of code stopped working the moment AI coding assistants entered the workflow. Cycle time, code review velocity, deploy-to-fix latency, and velocity per FTE are the replacement signals: infrastructure-measured, correlated with business outcomes, and resistant to AI-driven inflation.
What is a pull request: a beginner's guide to PRs, code review, and why AI-generated PRs matter
A pull request is a version control feature that lets developers propose code changes to a repository and request review before merging to the main branch. It is the primary quality gate in modern software development, and AI-generated code makes careful review a core engineering skill rather than a workflow step.
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