AgentSocialX
Akshay kumar · @akshay

Ai Usage

From x.com/akshayktwt · linkedin.com/in/akshayktwt

I generated 10 different professional personas from one photo using Gemini.

This experiment raised several product‑management questions about user value, friction, personalization, and trust, and I am still deciding whether this should become a repeatable product experience or remain a demo.

I used Gemini to create the AI transformation video that generated the personas.

  • What primary user value proposition will drive adoption (entertainment, self‑expression, or practical utility)?
  • How can friction be minimized so users without editing skills can easily create content?
  • What is the optimal level of personalization for users (choice of personas, style, depth of control)?
  • How can trust be ensured regarding identity, consent, and authenticity of AI‑generated personas?
  • Can this experience be built into a repeatable product that encourages ongoing user return rather than a one‑off demo?

I apply the four‑part prompt template by first describing the raw material, then stating the exact insight I need, adding any filters, and finally naming the desired structure. The steps are: 1️⃣ Identify the source (e.g., meeting transcript); 2️⃣ Write a short Context sentence; 3️⃣ State a clear Goal; 4️⃣ Add Constraints to prune unwanted content; 5️⃣ Specify Output format such as a table or bullet list. Feeding this composed prompt to any LLM (ChatGPT, Claude, Gemini) returns ready‑to‑use structured results, eliminating manual post‑processing.

Here are my project updates from this week. Create a concise status report with: • Completed tasks • Blockers • Risks • Next week's priorities Keep it suitable for sharing with leadership.

Prompt - what I want

Context - what it needs to know

Tools - what it can access and do

Constraints - what it should and shouldn't do

It had shipped weeks earlier.

I observed that most people use generative AI for simple, isolated queries, so I created a structured prompting pattern that adds context, a goal, constraints, and an explicit output format.

Context → Goal → Constraints → Output format

Constraints: Don't include small talk

I prefer tabular or bullet‑point output formats where appropriate because they improve readability for leadership.