The Art to Start (Designing Prompts for GPT-3). Introduction. | by Vlad Alex (Merzmensch) | Merzazine | Nov, 2020

Here, you will find the UX simplicity — and you don’t need more, really.

You have:

  • Prompt/Completion window
  • Preset bar
  • Settings
  • Submit button.

Preset bar — is useful if you designed presets you want to re-use or to share.

Settings — here, you can control the nature of the text:

  • Engine — GPT-3 provides four engines, which vary in output quality, latency (output speed) etc. “Ada” is the quickest one. “Davinci” is the most sophisticated engine — but sometimes you have to wait longer for text rendering.
    My choice: “Davinci”
    Note: with semantic search (I’ll write on this one day later), you can get relevant and quick results even with Ada.
  • Response Length (1–2048) — Length of the text, in tokens (approx. 1 token for 1 word, varies from engine to engine).
    My choice: 700–1000
    Note: Your input is calculated within 2048 tokens. The longer text you put in, the more appropriate becomes the output, but the smaller it is (everything must fit into 2048 tokens).
  • Temperature (0.0–1.0)— controls randomness, between boring redundancy and chaotic fiction. The higher the value, the more random texts appeared — but are still coherent, using Transformer’s self-attention.
    My use: 0.9 — in this case, it is not boring, but still not too repetitive.
    Note: try the same prompt with various temperature

As you may have noticed, I use the parameters above at the moment — I am already overwhelmed with the quality of variation between Length and Temperature. But you have even more ways to control the text (which is still not created) with the following settings:

  • Top P (0.0–1.0) — controls probability and diversity of completion
  • Frequency penalty (0.0–1.0) — looks for frequency among used tokens and decreases/increases the use of the same text patterns. The higher the value, the lesser is the possibility to get repeated patterns in the completion, compared with used tokens.
  • Presence Penalty (0.0–1.0) — By increasing the value, you can widen the possibility of new completion topics.
  • Best of (1–20) — generates x variants and shows the best one. Warning: it will use more of your credits than shown in the Completion, so use wisely

These settings are also suitable for saving specific presets, which works at the best for you — or experimenting with the same prompts but various parameters.

The last part is essential in case you generate some very structural text, like a chat. In the example on the left, you can see the settings of a default chat preset.

Stop sequences help GPT-3 to detect where to stop and to jump into another line.

Inject Start Text — this is the part of AI (in preset above shown as “AI”) — here the GPT-3 writes till it decides to stop + jump into the next line and provide your part:

You can see it as the precise determination of the characters.

In this case, GPT-3 wrote, “Hello, may I ask…”, jumped to the next line, and defined your part: “Human:”. Now it’s your turn. And after you write your text till end and click “SUBMIT” (alternatively CTRG+Enter), it will continue as “AI:”+GPT-3-written contents.

Note: if you delete Stop Sequences, GPT-3 will continue writing a dialog using “Characters” defined in Inject Start/Restart Text, but — unsupervised.

I used this method for the unsupervised dialog “AI Bots on the Run”. Which became a short movie:

As you see, there are various control options, which you may use to move the still unknown text into some specific direction to give precise nature to a text.

Using Show Probabilities, you can get insights into generated content with all the probabilities; you look into the Matrix, so to say:

But which texts can we generate now?

This is the topic of our the part… No, wait, I will talk about NOTHING in the next chapter, or rather: what happens with Tabula Rasa?

See you next time.

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