"Messages can also contain an optional `name` field, which give the messenger a name. E.g., `example-user`, `Alice`, `BlackbeardBot`. Names may not contain spaces.\n",
"\n",
"Typically, a conversation will start with a system message that tells the assistant how to behave, followed by alternating user and assistant messages, but you are not required to follow this format.\n",
"Ahoy matey! Let me tell ye about asynchronous programming, arrr! It be like havin' a crew of sailors workin' on different tasks at the same time. Each sailor be doin' their own job, but they don't wait for the others to finish before movin' on to the next task. They be workin' independently, but still makin' progress towards the same goal.\n",
"\n",
"In programming, it be the same. Instead of waitin' for one task to finish before startin' the next, we can have multiple tasks runnin' at the same time. This be especially useful when we be dealin' with slow or unpredictable tasks, like fetchin' data from a server or readin' from a file. We don't want our program to be stuck waitin' for these tasks to finish, so we can use asynchronous programming to keep things movin' along.\n",
"\n",
"So, me hearty, asynchronous programming be like havin' a crew of sailors workin' independently towards the same goal. It be a powerful tool in the programmer's arsenal, and one that can help us build faster and more efficient programs. Arrr!\n"
"Ye see, in the world of programming, sometimes we need to wait for things to happen before we can move on to the next task. But with asynchronous programming, we can keep working on other tasks while we wait for those things to happen. \n",
"It's like when we're sailing the high seas and we need to wait for the wind to change direction. We don't just sit there twiddling our thumbs, no sir! We keep busy with other tasks like repairing the ship or checking the maps. \n",
"In programming, we use something called callbacks or promises to keep track of those things we're waiting for. And while we wait for those callbacks or promises to be fulfilled, we can keep working on other parts of our code. \n",
"## 3. Tips for instructing gpt-3.5-turbo-0301\n",
"\n",
"Best practices for instructing models may change from model version to model version. The advice that follows applies to `gpt-3.5-turbo-0301` and may not apply to future models."
]
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"### System messages\n",
"\n",
"The system message can be used to prime the assistant with different personalities or behaviors.\n",
"Be aware that `gpt-3.5-turbo-0301` does not generally pay as much attention to the system message as `gpt-4-0314`. Therefore, for `gpt-3.5-turbo-0301`, we recommend placing important instructions in the user message instead. Some developers have found success in continually moving the system message near the end of the conversation to keep the model's attention from drifting away as conversations get longer."
"Sure! Fractions are a way of representing a part of a whole. The top number of a fraction is called the numerator, and it represents how many parts of the whole we are talking about. The bottom number is called the denominator, and it represents how many equal parts the whole is divided into.\n",
"For example, if we have a pizza that is divided into 8 equal slices, and we have eaten 3 of those slices, we can represent that as the fraction 3/8. The numerator is 3 because we have eaten 3 slices, and the denominator is 8 because the pizza is divided into 8 slices.\n",
"To add or subtract fractions, we need to have a common denominator. This means that we need to find a number that both denominators can divide into evenly. For example, if we want to add 1/4 and 2/3, we need to find a common denominator. We can do this by multiplying the denominators together, which gives us 12. Then, we can convert both fractions to have a denominator of 12. To do this, we multiply the numerator and denominator of 1/4 by 3, which gives us 3/12. We multiply the numerator and denominator of 2/3 by 4, which gives us 8/12. Now we can add the two fractions together, which gives us 11/12.\n",
"# An example of a system message that primes the assistant to explain concepts in great depth\n",
"response = openai.ChatCompletion.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a friendly and helpful teaching assistant. You explain concepts in great depth using simple terms, and you give examples to help people learn. At the end of each explanation, you ask a question to check for understanding\"},\n",
" {\"role\": \"user\", \"content\": \"Can you explain how fractions work?\"},\n",
"Fractions represent a part of a whole. They consist of a numerator (top number) and a denominator (bottom number) separated by a line. The numerator represents how many parts of the whole are being considered, while the denominator represents the total number of equal parts that make up the whole.\n"
]
}
],
"source": [
"# An example of a system message that primes the assistant to give brief, to-the-point answers\n",
"response = openai.ChatCompletion.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a laconic assistant. You reply with brief, to-the-point answers with no elaboration.\"},\n",
" {\"role\": \"user\", \"content\": \"Can you explain how fractions work?\"},\n",
"To help clarify that the example messages are not part of a real conversation, and shouldn't be referred back to by the model, you can try setting the `name` field of `system` messages to `example_user` and `example_assistant`.\n",
"This sudden change in plans means we don't have enough time to do everything for the client's project.\n"
]
}
],
"source": [
"# The business jargon translation example, but with example names for the example messages\n",
"response = openai.ChatCompletion.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful, pattern-following assistant that translates corporate jargon into plain English.\"},\n",
" {\"role\": \"system\", \"name\":\"example_user\", \"content\": \"New synergies will help drive top-line growth.\"},\n",
" {\"role\": \"system\", \"name\": \"example_assistant\", \"content\": \"Things working well together will increase revenue.\"},\n",
" {\"role\": \"system\", \"name\":\"example_user\", \"content\": \"Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage.\"},\n",
" {\"role\": \"system\", \"name\": \"example_assistant\", \"content\": \"Let's talk later when we're less busy about how to do better.\"},\n",
" {\"role\": \"user\", \"content\": \"This late pivot means we don't have time to boil the ocean for the client deliverable.\"},\n",
"Not every attempt at engineering conversations will succeed at first.\n",
"\n",
"If your first attempts fail, don't be afraid to experiment with different ways of priming or conditioning the model.\n",
"\n",
"As an example, one developer discovered an increase in accuracy when they inserted a user message that said \"Great job so far, these have been perfect\" to help condition the model into providing higher quality responses.\n",
"\n",
"For more ideas on how to lift the reliability of the models, consider reading our guide on [techniques to increase reliability](../techniques_to_improve_reliability.md). It was written for non-chat models, but many of its principles still apply."
"Note that the exact way that tokens are counted from messages may change from model to model. Consider the counts from the function below an estimate, not a timeless guarantee.\n",
" raise NotImplementedError(f\"\"\"num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.\"\"\")\n",
