Use Chat GPT to prepare for your next Interview
This could be the most helpful thing for people aspiring for new jobs.
A few prompts that can help you here are:
💡Prompt 1: Here is a Job denoscription of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}
💡Prompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}
💡Prompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?
💡Prompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?
💡Prompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?
Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166
ENJOY LEARNING 👍👍
This could be the most helpful thing for people aspiring for new jobs.
A few prompts that can help you here are:
💡Prompt 1: Here is a Job denoscription of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}
💡Prompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}
💡Prompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?
💡Prompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?
💡Prompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?
Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166
ENJOY LEARNING 👍👍
❤2👍2
Finish More Work in 2 Hours Than Most People Do in 2 Weeks
Here are 5 useful AI prompts that'll make you insanely productive:
Task Prioritizer
“Act like a productivity coach. I have 2 hours and this to-do list: [insert tasks]. Help me prioritize and assign time blocks.”
Instant Summary
“Summarize this [paste doc/text] and give me 3 action points with deadlines.”
Reply Generator
“Draft professional replies for these messages: [paste convos or emails]. Keep it short and clear.”
Meeting Prep
“Give me a 5-point agenda and questions to ask for a meeting on [topic]. Make me sound smart.”
Smart Research
“Give me a concise breakdown of [topic] in less than 200 words with real-world use cases.”
Use these with ChatGPT or any good AI tool — and you'll be ahead of 95% of people still stuck in busywork.
Here are 5 useful AI prompts that'll make you insanely productive:
Task Prioritizer
“Act like a productivity coach. I have 2 hours and this to-do list: [insert tasks]. Help me prioritize and assign time blocks.”
Instant Summary
“Summarize this [paste doc/text] and give me 3 action points with deadlines.”
Reply Generator
“Draft professional replies for these messages: [paste convos or emails]. Keep it short and clear.”
Meeting Prep
“Give me a 5-point agenda and questions to ask for a meeting on [topic]. Make me sound smart.”
Smart Research
“Give me a concise breakdown of [topic] in less than 200 words with real-world use cases.”
Use these with ChatGPT or any good AI tool — and you'll be ahead of 95% of people still stuck in busywork.
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LLM Cheatsheet
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
❤3
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Ready to level up your tech game without spending a rupee? These 6 full-length courses are beginner-friendly, 100% free, and packed with practical knowledge📚🧑🎓
Whether you want to code in Python, hack ethically, or build your first Android app — these videos are your shortcut to real tech skills📱💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42V73k4
Save this list and start crushing your tech goals today!✅️
❤1