Mahindra K's profile on Product Hunt

About

•Has 10+ years of experience in database development and support in MS SQL Server environments. •Experience in writing and troubleshoot T-SQL Queries,Stored Procedures, Functions, Tables, Views, Triggers, Indexes, Constraints. •Experience on Creation various type of reports such as drill down & drill through reports, Matrix reports, Sub reports, Parameterized Reports and Deployment of SSRS Reports. •Experience in Data Extraction, Transforming and Loading (ETL) using SQL Server Integration Services (SSIS), DTS, Bulk Insert, and BCP, creating ETL packages to extract data from different sources applying Transformations and load data to different destinations. •Experience in creating Jobs, Alerts, SQL Mail Agent, and schedule DTS Packages.

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Forums

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3d ago

If your product uses an LLM, how do you stop it from grading its own work?

I hit a bug recently that I think a lot of people building with LLMs

could run into, and I'm curious how others handle it.

My product checks whether academic citations point to real papers. When

a reference is too messy to parse, I ask an LLM to fill in the missing

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5d ago

How do you know if an AI’s polished text actually sounds like a human?

I m not a professional copywriter. I use AI to draft emails, newsletters, and social media posts in seconds. It s a massive time-saver, but it has one quiet risk: it all starts sounding the same.

A few weeks ago, I sent out a feedback email generated by AI. It read smoothly and looked professional on the first read. A day later, a client replied saying, "Did an algorithm write this? It doesn't sound like you at all." The words were grammatically correct, but the soul and voice were completely missing.

It made me wonder if we are trading our authentic voice for robotic perfection just because the grammar is clean.

How do you check AI-written copy before hitting send? Do you rewrite sections manually, run it through humanizers, or just accept the generic tone? And has an AI-written message ever backfired on you because it lacked a personal touch?

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5d ago

How do you turn vague user feedback into a useful next step?

When someone says a product feels confusing, that feedback is useful but hard to act on without more context. I think asking them to walk through what they were trying to do could reveal more than asking which feature they want.

For those who talk to users regularly, what follow-up question helps you get from this is confusing to something you can actually improve?