PROMPT ENGINEERING - AI Dynamics

AI-Powered Blog Title Generator: Creating Better Headlines

By

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22 December 2022 0h47

The title of your blog post is the first thing people see. 👀

That's why it's so important to get it right. This prompt suggests alternatives for a given blog title.

Try it on our Playground here: https://t.co/XPEcsX0GC5 pic.twitter.com/dqTj5Fvltg
— Cohere (@cohere) 21 décembre 2022

The title of your blog post is the first thing people see. That's why it's so important to get it right. This prompt suggests alternatives for a given blog title. Try it on our Playground here: https://
short.cohere.ai/SvYBbI

→ View original post on X — @cohere

22 December 2022

ChatGPT Guide: Understanding AI and Professional Applications

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@alliekmiller

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21 December 2022 22h39

Just published a 5500-word article on ChatGPT, what it is, why it’s important, and how professionals can use it in their daily lives Give it a read here

→ View original post on X — @alliekmiller

21 December 2022

Highly recommend this guide to prompt engineering

By

@goodside

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21 December 2022 18h59

Highly recommend this guide to prompt engineering — thorough, well referenced, up-to-date, cites my tweets:

→ View original post on X — @goodside

21 December 2022

Humans misjudge k-shot examples: they locate, not teach

By

@goodside

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21 December 2022 17h14

Humans have poor intuition re: the information content of k-shot examples. We often think we're teaching the model a task when really we're just locating it.

→ View original post on X — @goodside

21 December 2022

Invalid reasoning yields 80-90% of chain-of-thought gains

By

@goodside

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21 December 2022 17h14

Building on @sewon__min et al.'s find that multiple-choice examples with random answers barely harm performance, @BoshiWang2 et al. find you can achieve 80-90% the gains of chain-of-thought using only examples with invalid reasoning.

→ View original post on X — @goodside

21 December 2022

Fine-tuning Stable Diffusion: The Future of Image Generation

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@levelsio

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21 December 2022 6h02

@EmadGG was right, it'll all be about finetuning Stable Diffusion from here onwards

→ View original post on X — @levelsio

21 December 2022

Microsoft’s Structured Prompting Scales In-Context Learning to Thousands

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@jiqizhixin

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21 December 2022 2h49

Microsoft’s Structured Prompting Breaks In-Context Learning Length Limits, Scales to Thousands of Examples https://
syncedreview.com/2022/12/20/mic
rosofts-structured-prompting-breaks-in-context-learning-length-limits-scales-to-thousands-of-examples/
…

→ View original post on X — @jiqizhixin

21 December 2022

Fine-tuning vs. In-context Learning: Exploring Alternative Approaches

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@langchain

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20 December 2022 20h57

this is great question! i think its an unexplored space. finetuning is much more heavy weight (error prone, hard to do, more data required, etc), so it will be interesting to see if you can achieve similar performance just by using examples

→ View original post on X — @langchain

20 December 2022

MMR Example Selector for Optimal Contextual Diversity

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@langchain

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20 December 2022 17h06

Max Marginal Relevance (MMR) Example Selector Based on this preprint by @xiye_nlp et al, the MMR example selector chooses diverse, in-context examples H/t @johnjnay for surfacing this paper Docs: https://langchain.readthedocs.io/en/latest/examples/prompts/prompt_management.html Original Paper: https://x.com/xiye_nlp/status/1603821850592628738?s=20&t=wQPcRd8IJ8URbmytuYP7QA

→ View original post on X — @langchain

20 December 2022

LangChain Extends Prompt Formatting with Jinja Support

By

@langchain

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20 December 2022 17h05

Jinja Formatting Up until now, all the formatting in prompts has been done using Python f-strings But the template_format abstraction is pretty flexible, and @benderville easily extended it to support Jinja, a more advanced formatter Docs: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/prompt_management.html#Alternative-formats
…

→ View original post on X — @langchain

20 December 2022