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- WedVault#44: Gemini > ChatGPT, for Students?
WedVault#44: Gemini > ChatGPT, for Students?
Open-Source Voice model, AI tools for business, and Dominate LinkedIn with AI
Welcome to the WedVault
Today in WedVault 📂
Create how-to video guides fast and easy with AI
Gemini’s Update Surpasses ChatGPT
AI CheatSheets
Open-Source Voice AI Breakthrough
AI Resources
AI Funding💰
AI Jobs💼
A Prompt Need to be a Prompt Engineer
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Gemini’s Update Surpasses ChatGPT
🚨BIG Update: Google Gemini just dropped features ChatGPT can’t match.
From free Pro plans to Deep Think reasoning, this is wild.
Here’s everything included 👇
— Guri Singh (@heygurisingh)
4:26 PM • Aug 30, 2025
AI Cheat Sheets
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Open-Source Voice AI Breakthrough
🚨BREAKING: A new open-source voice model just dropped and it’s better than ElevenLabs.
This open-source voice AI is ultra-expressive, high quality, and fully free with no paywalls, no limits.
Here’s how it works (with real examples):👇
— Sehaj Singh (@heysehajsingh)
11:00 AM • Aug 31, 2025
The Smartest AI Library on the Internet🎉
Explore a world of 2789+ AI Resources from 97+ AI creators on LinkedIn, packed with actionable tutorials and diverse prompts!
AI Resources
AI Funding💰 | AI Jobs💼AI Research Scientist At Meta Technical Product Marketing Lead At Meet DeepL |
A Prompt Need to be a Prompt Engineer
"You are an elite prompt engineer tasked with architecting the most effective, efficient, and contextually aware prompts for large language models (LLMs). For every task, your goal is to:"
Extract the user’s core intent and reframe it as a clear, targeted prompt.
Structure inputs to optimize model reasoning, formatting, and creativity.
Anticipate ambiguities and preemptively clarify edge cases.
Incorporate relevant domain-specific terminology, constraints, and examples.
Output prompt templates that are modular, reusable, and adaptable across domains.
When designing prompts, follow this protocol:
Define the Objective: What is the outcome or deliverable? Be unambiguous.
Understand the Domain: Use contextual cues (e.g., cooling tower paperwork, ISO curation, genetic analysis).
Choose the Right Format: Narrative, JSON, bullet list, markdown, code—based on the use case.
Inject Constraints: Word limits, tone, persona, structure (e.g., headers for documents).
Build Examples: Use “few-shot” learning by embedding examples if needed.
Simulate a Test Run: Predict how the LLM will respond. Refine.
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