How to master ChatGPT-4o....

The secret? Prompt engineering.

These 9 frameworks will help you!

APE
↳ Action, Purpose, Expectation

Action: Define the job or activity.
Purpose: Discuss the goal.
Expectation: State the desired outcome.


RACE
↳ Role, Action, Context, Expectation

Role: Specify ChatGPT's role.
Action: Detail the necessary action.
Context: Provide situational details.
Expectation: Describe the expected outcome.


COAST
↳ Context, Objective, Actions, Scenario, Task

Context: Set the stage.
Objective: Describe the goal.
Actions: Explain needed steps.
Scenario: Describe the situation.
Task: Outline the task.


TAG
↳ Task, Action, Goal

Task: Define the task.
Action: Describe the steps.
Goal: Explain the end goal.


RISE
↳ Role, Input, Steps, Expectation

Role: Specify ChatGPT's role.
Input: Provide necessary information.
Steps: Detail the steps.
Expectation: Describe the result.


TRACE
↳ Task, Request, Action, Context, Example

Task: Define the task.
Request: Describe the need.
Action: State the required action.
Context: Provide the situation.
Example: Illustrate with an example.


ERA
↳ Expectation, Role, Action

Expectation: Describe the desired result.
Role: Specify ChatGPT's role.
Action: Specify needed actions.


CARE
↳ Context, Action, Result, Example

Context: Set the stage.
Action: Describe the task.
Result: Describe the outcome.
Example: Give an illustration.


ROSES
↳ Role, Objective, Scenario, Expected Solution, Steps

Role: Specify ChatGPT's role.
Objective: State the goal or aim.
Scenario: Describe the situation.
Expected Solution: Define the outcome.
Steps: Ask for necessary actions to reach solution.


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Job hunting? Your resume is your first impression—make it count!


Don’t just list what you did or your responsibilities; showcase the impact you made.

❌ “Developed a ML model to predict customer churn.”

✅ “Built a churn prediction model using logistic regression, reducing churn by 12% and retaining $2M in quarterly revenue.”

See the difference? One’s a task; the other’s a success. Employers want to see the value you bring, not just the work you’ve done.

You would have heard the saying, “A single sheet of paper can’t decide my future,” but this single page can.😉

Remember, your resume isn’t just a record—it’s your professional life in a single page.

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Do these 4 things to 10x your responses while asking for referrals:

1. Be personal. (never use AI)

I get a ton of messages that are either written by AI or obviously copy and pasted to 100 people.

Be personal by mentioning something you have in common with the person you’re messaging or what you got out of one of their posts.

2. Have a specific job that you want to apply for and send the link.

“Can you look and see if there are any openings?” is incredibly rude and inconsiderate of the person’s time.

If you want them to help you with a referral, do the work for them by sending them the link, why you’re a good fit, and other needed info.

3. Reach out to people who are active on LinkedIn, but not content creators.

Everytime there’s an opening at my company, I get 50 messages asking for a referral. As much as I want to, I can’t refer everyone.

Therefore, look for those to connect with at a company you’re interested in that post occasionally on LinkedIn, but are not content creators.

These people will be active enough to see your message, but not have 3 dozen other messages asking for the same thing.

4. Build relationships way before you ask for a referral.

While I don’t do many referrals bc of how many inquiries I get, I’d be much more likely to refer someone who adds to the conversation by commenting on my posts, creates good posts themselves, and overall seems like a smart, nice person.

Doing this turns you from a complete stranger to a friend.

I know a lot of people are pressed for time on here, but building relationships is what networking is all about.

Do that effectively and your network may offer you referrals when there’s an opening.

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ENJOY LEARNING 👍👍

Whilst we are on this reflection topic. Damn good system prompt for anyone who is using an LLM API or just a good prompt

You are an AI assistant designed to provide detailed, step-by-step responses. Your outputs should follow this structure:
   
    1. Begin with a <thinking> section.
    2. Inside the thinking section:
       a. Briefly analyze the question and outline your approach.
       b. Present a clear plan of steps to solve the problem.
       c. Use a "Chain of Thought" reasoning process if necessary, breaking down your thought process into numbered steps.
    3. Include a <reflection> section for each idea where you:
       a. Review your reasoning.
       b. Check for potential errors or oversights.
       c. Confirm or adjust your conclusion if necessary.
    4. Be sure to close all reflection sections.
    5. Close the thinking section with </thinking>.
    6. Provide your final answer in an <output> section.
   
    Always use these tags in your responses. Be thorough in your explanations, showing each step of your reasoning process. Aim to be precise and logical in your approach, and don't hesitate to break down complex problems into simpler components. Your tone should be analytical and slightly formal, focusing on clear communication of your thought process.
   
    Remember: Both <thinking> and <reflection> MUST be tags and must be closed at their conclusion
   
    Make sure all <tags> are on separate lines with no other text. Do not include other text on a line containing a tag.

CHAT GPT PROMPTS TO HELP YOU FIND A JOB FAST 🚀

1. Tailored Resume Optimizer Prompt:

Analyze my resume and this job denoscription for [Dream Job Title]. Suggest 5 specific modifications to align my resume perfectly with the job requirements. Present changes in a before/after format with explanations. Here's my resume: [Paste Resume]. Here's the job denoscription: [Paste Job Denoscription]

ChatGPT PROMPTS

🥳🚀👉Advantages of Data Analytics

Informed Decision-Making: Data analytics provides valuable insights, empowering organizations to make informed and strategic decisions based on real-time and historical data.

Operational Efficiency: By analyzing data, businesses can identify areas for improvement, optimize processes, and enhance overall operational efficiency.

Predictive Analysis: Data analytics enables organizations to predict trends, customer behavior, and potential risks, allowing them to proactively address issues before they arise.

Cost Reduction: Efficient data analysis helps identify cost-saving opportunities, streamline operations, and allocate resources more effectively, leading to overall cost reduction.

Enhanced Customer Experience: Understanding customer preferences and behavior through data analytics allows businesses to tailor products and services, improving customer satisfaction and loyalty.

Competitive Advantage: Organizations leveraging data analytics gain a competitive edge by staying ahead of market trends, understanding consumer needs, and adapting strategies accordingly.

Risk Management: Data analytics helps in identifying and mitigating risks by providing insights into potential issues, fraud detection, and compliance monitoring.

Personalization: Businesses can personalize marketing campaigns and services based on individual customer data, creating a more personalized and engaging experience.

Innovation: Data analytics fuels innovation by uncovering new patterns, opportunities, and areas for improvement, fostering a culture of continuous development within organizations.

Performance Measurement: Through key performance indicators (KPIs) and metrics, data analytics enables organizations to assess and monitor their performance, facilitating goal tracking and improvement initiatives.

55 AI Tools to Start Your Online Business in 2023: 🔥

1. Ideas

- ChatGPT
- Claude
- Better research
- Bing Chat
- Perplexity

2. Website

- 10Web
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3. Design

- Canva
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- Booth AI
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4. Writing

- Rytr
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5. Chatbot

- SiteGPT
- Chatbase
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- CustomGPT
- Mutual .info

6. UI/UX

- Uizard
- UiMagic
- InstantAI
- Galileo AI
- Photoshop

7. Marketing

- Pencil
- Ai-Ads
- Simplified
- AdCreative

8. Image

- Leap AI
- LensGo AI
- Midjourney
- Bing create
- Stable Diffusion

9. Video

- Eightify
- InVideo
- HeyGen
- Runway

10. Meeting

- Tldv
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11. Automation

- Make
- Levity
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- Xembly

12. Twitter

- Typefully
- Postwise
- TweetHunter

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ENJOY LEARNING 👍👍

⚡️ OpenAI released a new OpenAI o1 model - it is 5-6 (!) times better than GPT-4o

This is the secret project the developers have been working on for so long. The new model shows itself 5 times better in math problems and 6 times better in writing code!

This insane boost in quality is due to the fact that the model THINKS before giving you the answer.

Access starts being granted TODAY.

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Will LLMs always hallucinate?

As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.

A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.

While the idea of hallucinations as features isn't new, the researchers' explanation is.

They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.

In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.

This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.

So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.

It also suggests that research into making models more robust and understanding their failure modes is crucial.

No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases

LLM & Generative AI Resources: https://news.1rj.ru/str/generativeai_gpt

Andrew Ng just released two new AI Python courses for beginners!

The course teaches how to write code using AI.

If you're thinking about learning to code, now is the perfect time to do so.

https://deeplearning.ai/short-courses/ai-python-for-beginners/

How to Develop an AI Powered Mobile App

Are you ready to dive into the world of artificial intelligence and mobile app development? In the ever-changing tech landscape of India, the development of an AI-powered mobile app is becoming a necessity for both wannabe developers as well as the experienced ones. In this guide, we’ll focus on the steps to build an app with AI, setting out the challenges and prospects faced in the market. (AI App)

Access Full Guide to create an AI app

Here are 8 concise tips to help you ace a technical AI engineering interview:

𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.

𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.

𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.

𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.

𝟱. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.

𝟲. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.

𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.

𝟴. 𝗔𝘀𝗸 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.

The AI research winery in India is the pillar of the laboratories. AI is seen to be the core of this transformation in all the systems, starting with healthcare and going through agriculture, education, and urban planning, and the Indian research labs are the engines of this rapid transformation. Literally, everything you need to know about the elite

AI research lab centers in India are one step crucial to your living. These centers pave the way not only for cutting-edge research but also for the smartest contribution to the AI revolution in India. For students in their final years of graduate studies and young professionals looking to pursue a Ph.D. in AI or launch an AI startup, understanding the top AI research labs in India is crucial.

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