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
โAct like a productivity coach. I have 2 hours and this to-do list: [insert tasks]. Help me prioritize and assign time blocks.โ
Instant Summary
โSummarize this [paste doc/text] and give me 3 action points with deadlines.โ
Reply Generator
โDraft professional replies for these messages: [paste convos or emails]. Keep it short and clear.โ
Meeting Prep
โGive me a 5-point agenda and questions to ask for a meeting on [topic]. Make me sound smart.โ
Smart Research
โGive me a concise breakdown of [topic] in less than 200 words with real-world use cases.โ
Use these with ChatGPT or any good AI tool โ 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
โAct like a productivity coach. I have 2 hours and this to-do list: [insert tasks]. Help me prioritize and assign time blocks.โ
Instant Summary
โSummarize this [paste doc/text] and give me 3 action points with deadlines.โ
Reply Generator
โDraft professional replies for these messages: [paste convos or emails]. Keep it short and clear.โ
Meeting Prep
โGive me a 5-point agenda and questions to ask for a meeting on [topic]. Make me sound smart.โ
Smart Research
โGive me a concise breakdown of [topic] in less than 200 words with real-world use cases.โ
Use these with ChatGPT or any good AI tool โ and you'll be ahead of 95% of people still stuck in busywork.
โค6๐ฅ2
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
โค3
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