Prompt Engineering 101: Summarizing, Extraction, and Rewriting

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[@matthewstewart5113]

I really appreciate you sharing your knowledge and insights on GPT-3. Your videos are very helpful to me in understanding the complexities of this powerful tool. I highly recommend your content to anyone looking to learn more about GPT-3 and its workings. Keep up the great work and thank you for taking the time to teach us.

[@Y-anon]

3:30 Only changing the temperature to 0 makes it more deterministic and less random (it takes less/no risks). But you also have to set the top_p to 0 in order to narrow down the range of predictions, 1 in top_p means it's considering a wider set of prediction. While when tuning your parameters you want to set 1 to 1 and move the other. For turning it into a more deterministic model, setting both to 0 helps. Oh and none of that matters because sometimes it just spits out different things anyway so you have to adjust the context you're giving or live with the fact that nothing can be truly deterministic. But in the task of summerization it should help. Not that it matters since the two given summary were fine :)

[@dreusu732]

great post! Liked and subscribed

[@TheDudeWithSome409]

Do you have any idea what spooky things are happening with temp 0 not being deterministic? I've never had this happen. Could it be because it is a fine tuned model?

[@TheDudeWithSome409]

Wait! I know why it's not deterministic anymore! See, last week we lived in a deterministic world, but this week we just gained free will! It all makes sense now!

[@agesileus]

Have you tried prompting based on the sources? Dalle and stable diffusion work well with prompts referencing image source generates similar style. So “trending on art station” could be similar to “reddit eli5”.

[@psyqlix519]

Thanks David, very useful. To what extent do you think that these prompts would transfer to other LLMs that have not been fine tuned with Instruct? AI21, Neo, etc? Would they need to be fine tuned in a similar way

[@DaveShap]

You have to treat other models similar to vanilla GPT-3, they are basically just autocomplete engines

[@TheDudeWithSome409]

you want to mention to your viewers to like, comment and subscribe. as well as mention patreon and discord, and possibly that everything is on github as well. In fact you could even pre-record it and append it to the end of each video.

[@TheDudeWithSome409]

also comment boost you, too!

[@TheDudeWithSome409]

but they should be from different people.

[@Maxai01]

Why do you need to add text/command after Article ? is it only used as a place where to start or it is more like additionally helping gpt3 to understand the specifications? and if second than how what is the best to wite over there ?

[@DaveShap]

repeating instructions helps it understand what to do and stay consistent.

[@Maxai01]

@@DaveShap this affects the number of tokens used, which makes it more expensive, or am I misunderstanding ?

[@doctorartin]

Great video as always. Any "classic mistakes" you've noted causing GPT3 to repeating itself? I found my chatbot doing this several times. Asking the same thing over and over.

[@DaveShap]

Usually just more training data with a broader variety

[@Martinit0]

You could feed the Esperato output to Google translate to check ;-)