@ -15,9 +15,9 @@ The magic of large language models is that by being trained to minimize this pre
* how to code
* etc.
None of these capabilities are explicitly programmed in—they all emerge as a result of training.
They do this by “reading” a large amount of existing text and learning how words tend to appear in context with other words, and uses what it has learned to predict the next most likely word that might appear in response to a user request, and each subsequent word after that.
GPT-3 powers [hundreds of software products][GPT3 Apps Blog Post], including productivity apps, education apps, games, and more.
GPT-3 and GPT-4 power [many software products][OpenAI Customer Stories], including productivity apps, education apps, games, and more.
## How to control a large language model
@ -27,6 +27,7 @@ Large language models can be prompted to produce output in a few ways:
* **Instruction**: Tell the model what you want
* **Completion**: Induce the model to complete the beginning of what you want
* **Scenario**: Give the model a situation to play out
* **Demonstration**: Show the model what you want, with either:
* A few examples in the prompt
* Many hundreds or thousands of examples in a fine-tuning training dataset
@ -35,7 +36,7 @@ An example of each is shown below.
### Instruction prompts
Instruction-following models (e.g., `text-davinci-003` or any model beginning with `text-`) are specially designed to follow instructions. Write your instruction at the top of the prompt (or at the bottom, or both), and the model will do its best to follow the instruction and then stop. Instructions can be detailed, so don't be afraid to write a paragraph explicitly detailing the output you want.
Write your instruction at the top of the prompt (or at the bottom, or both), and the model will do its best to follow the instruction and then stop. Instructions can be detailed, so don't be afraid to write a paragraph explicitly detailing the output you want, just stay aware of how many [tokens](https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them) the model can process.
Example instruction prompt:
@ -71,6 +72,24 @@ Output:
Ted Chiang
```
### Scenario prompt example
Giving the model a scenario to follow or role to play out can be helpful for complex queries or when seeking imaginative responses. When using a hypothetical prompt, you set up a situation, problem, or story, and then ask the model to respond as if it were a character in that scenario or an expert on the topic.
Example scenario prompt:
```text
Your role is to extract the name of the author from any given text
“Some humans theorize that intelligent species go extinct before they can expand into outer space. If they're correct, then the hush of the night sky is the silence of the graveyard.”
― Ted Chiang, Exhalation
```
Output:
```text
Ted Chiang
```
### Demonstration prompt example (few-shot learning)
Similar to completion-style prompts, demonstrations can show the model what you want it to do. This approach is sometimes called few-shot learning, as the model learns from a few examples provided in the prompt.
@ -118,17 +137,15 @@ Output:
## Code Capabilities
Large language models aren't only great at text - they can be great at code too. OpenAI's specialized code model is called [Codex].
Large language models aren't only great at text - they can be great at code too. OpenAI's [GPT-4][GPT-4 and GPT-4 Turbo] model is a prime example.
Codex powers [more than 70 products][Codex Apps Blog Post], including:
* [GitHub Copilot] (autocompletes code in VS Code and other IDEs)
* [Pygma](https://pygma.app/) (turns Figma designs into code)
* [Replit](https://replit.com/) (has an 'Explain code' button and other features)
* [Warp](https://www.warp.dev/) (a smart terminal with AI command search)
* [Machinet](https://machinet.net/) (writes Java unit test templates)
* [GitHub Copilot] (autocompletes code in Visual Studio and other IDEs)
* [Replit](https://replit.com/) (can complete, explain, edit and generate code)
* [Cursor](https://cursor.sh/) (build software faster in an editor designed for pair-programming with AI)
Note that unlike instruction-following text models (e.g., `text-davinci-002`), Codex is *not* trained to follow instructions. As a result, designing good prompts can take more care.
GPT-4 is more advanced than previous models like `text-davinci-002`. But, to get the best out of GPT-4 for coding tasks, it's still important to give clear and specific instructions. As a result, designing good prompts can take more care.
### More prompt advice
@ -136,17 +153,17 @@ For more prompt examples, visit [OpenAI Examples][OpenAI Examples].
In general, the input prompt is the best lever for improving model outputs. You can try tricks like:
* **Give more explicit instructions.** E.g., if you want the output to be a comma separated list, ask it to return a comma separated list. If you want it to say "I don't know" when it doesn't know the answer, tell it 'Say "I don't know" if you do not know the answer.'
* **Supply better examples.** If you're demonstrating examples in your prompt, make sure that your examples are diverse and high quality.
* **Ask the model to answer as if it was an expert.** Explicitly asking the model to produce high quality output or output as if it was written by an expert can induce the model to give higher quality answers that it thinks an expert would write. E.g., "The following answer is correct, high-quality, and written by an expert."
* **Prompt the model to write down the series of steps explaining its reasoning.**E.g., prepend your answer with something like "[Let's think step by step](https://arxiv.org/pdf/2205.11916v1.pdf)." Prompting the model to give an explanation of its reasoning before its final answer can increase the likelihood that its final answer is consistent and correct.
* **Be more specific** E.g., if you want the output to be a comma separated list, ask it to return a comma separated list. If you want it to say "I don't know" when it doesn't know the answer, tell it 'Say "I don't know" if you do not know the answer.' The more specific your instructions, the better the model can respond.
* **Provide Context**: Help the model understand the bigger picture of your request. This could be background information, examples/demonstrations of what you want or explaining the purpose of your task.
* **Ask the model to answer as if it was an expert.** Explicitly asking the model to produce high quality output or output as if it was written by an expert can induce the model to give higher quality answers that it thinks an expert would write. Phrases like "Explain in detail" or "Describe step-by-step" can be effective.
* **Prompt the model to write down the series of steps explaining its reasoning.**If understanding the 'why' behind an answer is important, prompt the model to include its reasoning. This can be done by simply adding a line like "[Let's think step by step](https://arxiv.org/abs/2205.11916)" before each answer.
Kesku
5 months ago
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