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Prompt engineering and large language models are a fairly nascent field, so new ways to hack around them are being discovered every day. The two large classes o

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A large language model is a prediction engine that takes a sequence of words and tries to predict the most likely sequence to come after that sequence[^1]. It d

Sometimes you’ll want the bot to have the capability to perform actions on the user’s behalf, like adding a memo to a receipt or plotting a chart. Or perhaps we

> * Come up with 50 brilliant ideas for a `business around dental hygiene`. > * Give me 50 `uses for a paperclip`. > * Research `The Golden Girls` `TV show`.

Tells you when the article it's summarizing doesn't contain the info you want. > * Answer the following questions based on information `from the same article` a

> * Tell me about `online tracking from Big Tech companies`. > * Yes, that is exactly the topic I would like more information on. Could you pull information fro

Sometimes no matter what tricks you throw at the model, it just won’t do what you want it to do. In these scenarios you can **sometimes** fallback to fine-tunin

包括但不限于： | 类别 | 描述 | |:----:|--------------------------------------------------

> :pushpin: Skip [to here](#what-is-a-prompt) if you'd like to jump past the > history of language models. This section is for the curious minded, though > may

In hidden contexts, you’ll frequently want to embed all sorts of data. The specific strategy will vary depending on the type and quantity of data you are embedd

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The “give a bot a fish” bucket is for scenarios when you can explicitly give the bot, in the hidden context, all of the information it needs to do whatever task

Note: 250 words = 1 page; 1250 words = 5 pages; etc. #### All at one time > * Write a `1000-word` essay on how `innovations are adopted in a specific industry

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Up above, we used an analogy of prompts as the “source code” that a language model “interprets”. **Prompt engineering is the art of writing prompts to get the l

Frequently, a natural language response isn’t sufficient on its own and you’ll want the model’s output to cite where it is getting data from. One useful thing

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