3) Generate content with a specific purpose or goal in mind Tell ChatGPT who your audience is and what you want to achieve with your content. Remember, it has no context about who you are or what you want unless you give it some. So give it context. Example prompt:
– Topic:
GENERATIVE AI
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Generate content with a specific purpose for ChatGPT
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Using ChatGPT to vary writing formats
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2) Use ChatGPT to write in different formats Ask ChatGPT to vary its output.
• Outline • Mind map • Bullet points • Persuasive essay • Chunks of text of less than 280 characters • Using the structure: 1) What, 2) Why, 3) How Create a mind map on the topic of using -
10 prompt engineering strategies boosted ChatGPT output quality by 300%
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I thought I knew ChatGPT. Then I tried these 10 prompt engineering strategies. Its output quality increased 300%. Follow the thread:
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ChatGPT: Writing from Different Perspectives for Entrepreneurs
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1) Have ChatGPT write from different perspectives: Ask it to write from the perspective of a group of characters with different backgrounds or viewpoints. Explore new ideas and perspectives, and add depth to your writing. Example prompt: Topic: Productivity for entrepreneurs
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Grok’s Potential: Personalized AI Using Twitter Data
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grok would blow up if it used your twitter data to personalize it instead of focusing on world events. they have the data to: > recall, cross reference posts you've seen, liked
> summarize discussions relevant to you today
> find people you should meet – like fb graph search v2 -
ChatGPT Updating Memory Management and Personalization Features
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NEW: ChatGPT keeps working on its Personalisation feature by adding a button to manage its personalized memory. The UI hints that you will be able to ask Chat GPT explicitly what it remembers about you and it will allow you to reset it.
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AI-Powered Hologram Technology by Holoconnects
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3. AI-powered Hologram by Holoconnects pic.twitter.com/GrrJitfcES
— The Rundown AI (@TheRundownAI) 21 janvier 20243. AI-powered Hologram by Holoconnects
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Production-Ready End-to-End RAG Implementation Guide
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Spoiler alert: it’s easier than you think to implement a production-ready end-to-end RAG. In this demo, you’ll learn about data conversion, embedding models, and efficient hosting with Databricks Model Serving. Watch now https://
bit.ly/3RDHIFH -
ReFT: Enhancing LLM Reasoning Through Reinforced Fine-Tuning
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6/ Reasoning with Reinforced Fine-Tuning – an approach, ReFT, to enhance the generalizability of LLMs for reasoning; it starts with applying SFT and then applies online RL for further refinement while automatically sampling reasoning paths to learn from.
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Self-Rewarding Models: LLM Self-Alignment Training Method
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4/ Self-Rewarding Models – proposes a self-alignment method that uses the model itself for LLM-as-a-Judge prompting to provide its rewards during training; Iterative DPO is used for instruction following training using the preference pairs.