2. Use the 80/20 principle to learn faster Prompt: "Using the 80/20 rule, identify the key 20% of concepts in [Subject] that will give me 80% of the results and create a focused learning plan."
PROMPT ENGINEERING
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Using ChatGPT to create a personalized learning path
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1. Learn and develop any new skill Prompt: "ChatGPT, help me design a step-by-step learning path to master [Skill Name] starting from basics to advanced levels."
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6 ChatGPT Prompts to Save Time in 2024
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6 ChatGPT prompts that'll save you hundreds of hours in 2024: (Bookmark this for later)
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Optimizing prompts for blog posts using different LLM models
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Working on a better prompt for blog posts on TestingCatalog. Currently using @hunchtools canvas to play around with outputs provided by different models (gpt4 turbo, claude3 opus, gemini pro). Quite useful to compare how these models perform and optimise the prompt to make it
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ChatGPT Performance Optimization: Prompting Techniques and Strategies
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Some options for… When chatgpt doesn’t want to do it:
– demand: “try harder”
– resourceful: “find a way”
– continuation: “you’ve done it before”
– guilt: “you promised me you would do it” When you want better chatgpt performance:
– show examples
– try a better model
– ask it -
Retrieval-Augmented Fine-Tuning: Combining RAG and Fine-tuning
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10/ Retrieval-Augmented Fine-Tuning Combines the benefits of RAG and fine-tuning to improve a model's ability to answer questions in "open-book" in-domain settings; combining it with RAFT's CoT-style response helps to improve reasoning.
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Agent-FLAN: Fine-tuning Language Models for Enhanced Agent Performance
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7/ Agent-FLAN Designs data and methods to effectively fine-tune language models for agents, referred to as Agent-FLAN; this enables Llama2-7B to outperform prior best works by 3.5% across various agent evaluation datasets.
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RankPrompt: Self-Ranking Method for Better LLM Reasoning
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5/ Step-by-Step Comparisons Make LLMs Better Reasoners Proposes RankPrompt, a prompting method to enable LLMs to self-rank their responses without additional resources.
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Tool Use in Large Language Models: Overview and Applications
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4/ Tool Use in LLMs Provides an overview of tool use in LLMs, including a formal definition of the tool-use paradigm, scenarios where LLMs leverage tool usage, and for which tasks this approach works well.
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Rating LLM outputs: hand-labeling versus prompt-based evaluation
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Determine what you care about (ex: conciseness, factuality, style, etc.). The either:
– hand rate a bunch of examples on each dimension, train a model to understand what to look for
– build a LLM prompt with Mistral or similar to do this without fine-tuning Run over all the