Studyhobby

Frontend Developer

Helping brands to grow || Open for collaboration|| Linkedin Growth || DM for promotion || Python || C/C++ || Java || Frontend Developer || Blockchain || SQL || React || AI

No replies. No interviews. Just silence for 90 days.

Then, I ran my resume through ChatGPT.

Got 5 callbacks in less than a week-using just 8 prompts.

[Save for later]

1. Spot the Flaws

Prompt: Act as a recruiter for [your industry/role]. Review my resume below and highlight weak areas, overused buzzwords, and missing metrics. Be brutally honest.

2. Rewrite for Impact

Prompt: Rewrite this resume to sound more results-driven, quantifiable, and compelling for [target role]. Focus on achievements, not just duties.

3. ATS Boost

Prompt: Update this resume to be fully optimized for Applicant Tracking Systems (ATS) for the role of [specific role/title]. Use industry-specific keywords naturally.

4. Craft My Hook

Prompt: Write a powerful, 3-line professional summary that hooks a recruiter in under 10 seconds. Prioritize impact, clarity, and value.


5. Upgrade Experience

Prompt: Rephrase the experience section to highlight impact, results, and transferable skills using action verbs and quantifiable outcomes.


6. Format Fix

Prompt: Suggest a clean, modern resume format that works for both humans and ATS. No graphics, no columns. Just structured and effective.

7. Tailor for the Role

Prompt: Tailor this resume to fit this specific job description [paste JD]. Highlight matching experience and reword sections to match the language used.


8. Standout Cover Letter

Prompt: Write a compelling cover letter based on this resume and job description. Keep it personal, enthusiastic, and under 200 words.

𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗿𝗲𝗴𝗿𝗲𝘁 𝗻𝗼𝘁 𝘁𝗮𝗸𝗶𝗻𝗴 𝗶𝗻 𝟐𝟎𝟐𝟓.

🟩Join my telegram for the Job updates & Notes- https://lnkd.in/dkg-GWWD

1. Microsoft Azure Data Scientist Associate 
🔗https://lnkd.in/gr7SUQ4F

2. Microsoft Cybersecurity Analyst Professional
🔗https://lnkd.in/gt-43Unw

3. Microsoft Power BI Data Analyst Professional
🔗https://lnkd.in/gsGsjTFN

4. Microsoft Azure Data Engineering Associate (DP-203) Professional
 🔗https://lnkd.in/gUmwAMy6

5. Microsoft Azure Developer Associate (AZ-204) Professional
🔗 https://lnkd.in/gTqqFZjU

6. Microsoft Azure Security Engineer Associate (AZ-500) Professional
🔗https://lnkd.in/gr_WGCwr

7. Advanced Google Analytics
🔗https://lnkd.in/gWVRerfJ

8. Google Project Management
🔗https://lnkd.in/gdS8eJKh

9. Agile Project Management
🔗https://lnkd.in/gtqPQXT9

10. Project Execution: Running the Project
🔗https://lnkd.in/gf-K3Ytm

11. Foundations of Project Management
🔗https://lnkd.in/gCCSMHju

12. Project Initiation: Starting a Successful Project
🔗https://lnkd.in/gkXWs9tz

13. Project Planning: Putting It All Together
🔗https://lnkd.in/gcENMpci

14. Google Data Analytics:
🔗https://lnkd.in/gXQXi9Pz

15. Get Started with Python
🔗https://lnkd.in/gRnJVg8r

16. Crash Course on Python
🔗 https://lnkd.in/gKwrfxei

17. Data Analysis with R Programming
🔗 https://lnkd.in/gEsnMEz9

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Rushika Rai

Frontend Developer || Resume Writer || Linkedin account Growth || Content Creator || AI || Helping Brands to Grow ||Angular || GitHub || Figma

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Bansari Kikani

Most teams rush to “build an AI agent”…
but they forget one thing:

AI agents don’t fail because of weak models,
they fail because of weak planning, poor guardrails, and zero maintenance.

👉 An agent is only as strong as the system you build around it.
Not the model.
Not the prompt.
The system.

If you can nail these 6 stages, you’ll build agents that actually work in the real world - reliably, safely, and at scale.

Here are the 6 stages of AI Agent Development:

1. Planning - Don’t start building yet.
Define the business need, the agent’s objective, resources, and risks.
This is where you ensure the agent deserves to exist.

2. Design - Build the blueprint.
Choose the right framework, model, grounding strategy, and guardrails.
Good design prevents 90% of future failures.

3. Development - Bring the agent to life.
Build the agent logic, integrate models, fine-tune if needed, and prepare documentation.
This is where the intelligence starts taking shape.

4. Testing - Try to break it (before users do).
Run performance tests, integration tests, edge-case testing, and allocate compute.
If it passes here, it won’t embarrass you in production.

5. Deployment - Let it fly.
Launch the agent, confirm guardrails, monitor behaviour, and ensure compliance.
Real-world context starts shaping performance.

6. Maintenance - The stage everyone forgets.
Monitor objectives, optimise workflows, and act on user feedback.
Agents get smarter only if you maintain them.

AI agents aren’t “build once and done.”
They are living systems - they evolve based on data, feedback, and context.

If you follow these 6 stages, you aren’t just building an agent…
you’re building an autonomous, reliable digital worker.

A few courses that particularly stood out to me:
1. Python for Everybody – University of Michigan: https://lnkd.in/gVPha9EE
2. Introduction to Generative AI: https://lnkd.in/detitq8h
3. Generative adversarial Networks: https://lnkd.in/dyXgBExM
4. Introduction to Ai: https://lnkd.in/d2Awst5W
5. Google Data Analytics: https://lnkd.in/gy9NN-TR
6. Google Project Management: https://lnkd.in/gPBFHwvK
7. Google IT Support: https://lnkd.in/gKiyT7_9
8. Foundations of Cybersecurity: https://lnkd.in/ghDzTw66
9. Google UX Design: https://lnkd.in/gDAP7urj
10. Data Science & Generative AI Fundamentals – IBM: https://lnkd.in/gD8ZqWZN
11. Excel Skills for Business – Macquarie University: https://lnkd.in/gGaKTCkS

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Frontend Developer || Resume Writer || Personal Branding || Content Creator || AI || Open for collaboration ||Angular || GitHub || Figma

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