How to work with the ChatGPT and GPT-4 models - Azure OpenAI Service (2023)

  • Article

The ChatGPT and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, meaning they accepted a prompt string and returned a completion to append to the prompt. However, the ChatGPT and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format, and return a completion that represents a model-written message in the chat. While this format was designed specifically for multi-turn conversations, you'll find it can also work well for non-chat scenarios too.

In Azure OpenAI there are two different options for interacting with these type of models:

  • Chat Completion API.
  • Completion API with Chat Markup Language (ChatML).

The Chat Completion API is a new dedicated API for interacting with the ChatGPT and GPT-4 models. This API is the preferred method for accessing these models. It is also the only way to access the new GPT-4 models.

ChatML uses the same completion API that you use for other models like text-davinci-002, it requires a unique token based prompt format known as Chat Markup Language (ChatML). This provides lower level access than the dedicated Chat Completion API, but also requires additional input validation, only supports ChatGPT (gpt-35-turbo) models, and the underlying format is more likely to change over time.

This article walks you through getting started with the new ChatGPT and GPT-4 models. It's important to use the techniques described here to get the best results. If you try to interact with the models the same way you did with the older model series, the models will often be verbose and provide less useful responses.

Working with the ChatGPT and GPT-4 models

The following code snippet shows the most basic way to use the ChatGPT and GPT-4 models with the Chat Completion API. If this is your first time using these models programmatically, we recommend starting with our .

GPT-4 models are currently only available by request. Existing Azure OpenAI customers can apply for access by filling out this form.

import osimport openaiopenai.api_type = "azure"openai.api_version = "2023-05-15" openai.api_base = os.getenv("OPENAI_API_BASE") # Your Azure OpenAI resource's endpoint value.openai.api_key = os.getenv("OPENAI_API_KEY")response = openai.ChatCompletion.create( engine="gpt-35-turbo", # The deployment name you chose when you deployed the ChatGPT or GPT-4 model. messages=[ {"role": "system", "content": "Assistant is a large language model trained by OpenAI."}, {"role": "user", "content": "Who were the founders of Microsoft?"} ])print(response)print(response['choices'][0]['message']['content'])

Output

{ "choices": [ { "finish_reason": "stop", "index": 0, "message": { "content": "The founders of Microsoft are Bill Gates and Paul Allen. They co-founded the company in 1975.", "role": "assistant" } } ], "created": 1679014551, "id": "chatcmpl-6usfn2yyjkbmESe3G4jaQR6bsScO1", "model": "gpt-3.5-turbo-0301", "object": "chat.completion", "usage": { "completion_tokens": 86, "prompt_tokens": 37, "total_tokens": 123 }}

Note

The following parameters aren't available with the new ChatGPT and GPT-4 models: logprobs, best_of, and echo. If you set any of these parameters, you'll get an error.

Every response includes a finish_reason. The possible values for finish_reason are:

  • stop: API returned complete model output.
  • length: Incomplete model output due to max_tokens parameter or token limit.
  • content_filter: Omitted content due to a flag from our content filters.
  • null:API response still in progress or incomplete.

Consider setting max_tokens to a slightly higher value than normal such as 300 or 500. This ensures that the model doesn't stop generating text before it reaches the end of the message.

Model versioning

Note

(Video) ChatGPT & OpenAI powering your apps | OpenAI Studio in Microsoft Azure

gpt-35-turbo is equivalent to the gpt-3.5-turbo model from OpenAI.

Unlike previous GPT-3 and GPT-3.5 models, the gpt-35-turbo model as well as the gpt-4 and gpt-4-32k models will continue to be updated. When creating a deployment of these models, you'll also need to specify a model version.

Currently, only version 0301 is available for ChatGPT and 0314 for GPT-4 models. We'll continue to make updated versions available in the future. You can find model deprecation times on our models page.

Working with the Chat Completion API

OpenAI trained the ChatGPT and GPT-4 models to accept input formatted as a conversation. The messages parameter takes an array of dictionaries with a conversation organized by role.

The format of a basic Chat Completion is as follows:

{"role": "system", "content": "Provide some context and/or instructions to the model"},{"role": "user", "content": "The users messages goes here"}

A conversation with one example answer followed by a question would look like:

{"role": "system", "content": "Provide some context and/or instructions to the model."},{"role": "user", "content": "Example question goes here."},{"role": "assistant", "content": "Example answer goes here."},{"role": "user", "content": "First question/message for the model to actually respond to."}

System role

The system role also known as the system message is included at the beginning of the array. This message provides the initial instructions to the model. You can provide various information in the system role including:

  • A brief description of the assistant
  • Personality traits of the assistant
  • Instructions or rules you would like the assistant to follow
  • Data or information needed for the model, such as relevant questions from an FAQ

You can customize the system role for your use case or just include basic instructions. The system role/message is optional, but it's recommended to at least include a basic one to get the best results.

Messages

After the system role, you can include a series of messages between the user and the assistant.

 {"role": "user", "content": "What is thermodynamics?"}

To trigger a response from the model, you should end with a user message indicating that it's the assistant's turn to respond. You can also include a series of example messages between the user and the assistant as a way to do few shot learning.

Message prompt examples

The following section shows examples of different styles of prompts that you could use with the ChatGPT and GPT-4 models. These examples are just a starting point, and you can experiment with different prompts to customize the behavior for your own use cases.

Basic example

If you want the ChatGPT model to behave similarly to chat.openai.com, you can use a basic system message like "Assistant is a large language model trained by OpenAI."

{"role": "system", "content": "Assistant is a large language model trained by OpenAI."},{"role": "user", "content": "Who were the founders of Microsoft?"}

Example with instructions

For some scenarios, you may want to give additional instructions to the model to define guardrails for what the model is able to do.

{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer their tax related questions.Instructions: - Only answer questions related to taxes. - If you're unsure of an answer, you can say "I don't know" or "I'm not sure" and recommend users go to the IRS website for more information. "},{"role": "user", "content": "When are my taxes due?"}

Using data for grounding

You can also include relevant data or information in the system message to give the model extra context for the conversation. If you only need to include a small amount of information, you can hard code it in the system message. If you have a large amount of data that the model should be aware of, you can use embeddings or a product like Azure Cognitive Search to retrieve the most relevant information at query time.

{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Serivce. Only answer questions using the context below and if you're not sure of an answer, you can say 'I don't know'.Context:- Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series.- Azure OpenAI Service gives customers advanced language AI with OpenAI GPT-3, Codex, and DALL-E models with the security and enterprise promise of Azure. Azure OpenAI co-develops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.- At Microsoft, we're committed to the advancement of AI driven by principles that put people first. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes requiring applicants to show well-defined use cases, incorporating Microsoft’s principles for responsible AI use."},{"role": "user", "content": "What is Azure OpenAI Service?"}

Few shot learning with Chat Completion

You can also give few shot examples to the model. The approach for few shot learning has changed slightly because of the new prompt format. You can now include a series of messages between the user and the assistant in the prompt as few shot examples. These examples can be used to seed answers to common questions to prime the model or teach particular behaviors to the model.

This is only one example of how you can use few shot learning with ChatGPT and GPT-4. You can experiment with different approaches to see what works best for your use case.

{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer their tax related questions. "},{"role": "user", "content": "When do I need to file my taxes by?"},{"role": "assistant", "content": "In 2023, you will need to file your taxes by April 18th. The date falls after the usual April 15th deadline because April 15th falls on a Saturday in 2023. For more details, see https://www.irs.gov/filing/individuals/when-to-file."},{"role": "user", "content": "How can I check the status of my tax refund?"},{"role": "assistant", "content": "You can check the status of your tax refund by visiting https://www.irs.gov/refunds"}

Using Chat Completion for non-chat scenarios

The Chat Completion API is designed to work with multi-turn conversations, but it also works well for non-chat scenarios.

(Video) Azure OpenAI GPT-4 Tutorial

For example, for an entity extraction scenario, you might use the following prompt:

{"role": "system", "content": "You are an assistant designed to extract entities from text. Users will paste in a string of text and you will respond with entities you've extracted from the text as a JSON object. Here's an example of your output format:{ "name": "", "company": "", "phone_number": ""}"},{"role": "user", "content": "Hello. My name is Robert Smith. I'm calling from Contoso Insurance, Delaware. My colleague mentioned that you are interested in learning about our comprehensive benefits policy. Could you give me a call back at (555) 346-9322 when you get a chance so we can go over the benefits?"}

Creating a basic conversation loop

The examples so far have shown you the basic mechanics of interacting with the Chat Completion API. This example shows you how to create a conversation loop that performs the following actions:

  • Continuously takes console input, and properly formats it as part of the messages array as user role content.
  • Outputs responses that are printed to the console and formatted and added to the messages array as assistant role content.

This means that every time a new question is asked, a running transcript of the conversation so far is sent along with the latest question. Since the model has no memory, you need to send an updated transcript with each new question or the model will lose context of the previous questions and answers.

import osimport openaiopenai.api_type = "azure"openai.api_version = "2023-05-15" openai.api_base = os.getenv("OPENAI_API_BASE") # Your Azure OpenAI resource's endpoint value .openai.api_key = os.getenv("OPENAI_API_KEY")conversation=[{"role": "system", "content": "You are a helpful assistant."}]while(True): user_input = input() conversation.append({"role": "user", "content": user_input}) response = openai.ChatCompletion.create( engine="gpt-3.5-turbo", # The deployment name you chose when you deployed the ChatGPT or GPT-4 model. messages = conversation ) conversation.append({"role": "assistant", "content": response['choices'][0]['message']['content']}) print("\n" + response['choices'][0]['message']['content'] + "\n")

When you run the code above you will get a blank console window. Enter your first question in the window and then hit enter. Once the response is returned, you can repeat the process and keep asking questions.

Managing conversations

The previous example will run until you hit the model's token limit. With each question asked, and answer received, the messages array grows in size. The token limit for gpt-35-turbo is 4096 tokens, whereas the token limits for gpt-4 and gpt-4-32k are 8192 and 32768 respectively. These limits include the token count from both the message array sent and the model response. The number of tokens in the messages array combined with the value of the max_tokens parameter must stay under these limits or you'll receive an error.

It's your responsibility to ensure the prompt and completion falls within the token limit. This means that for longer conversations, you need to keep track of the token count and only send the model a prompt that falls within the limit.

The following code sample shows a simple chat loop example with a technique for handling a 4096 token count using OpenAI's tiktoken library.

The code requires tiktoken 0.3.0. If you have an older version run pip install tiktoken --upgrade.

import tiktokenimport openaiimport osopenai.api_type = "azure"openai.api_version = "2023-05-15" openai.api_base = os.getenv("OPENAI_API_BASE") # Your Azure OpenAI resource's endpoint value .openai.api_key = os.getenv("OPENAI_API_KEY")system_message = {"role": "system", "content": "You are a helpful assistant."}max_response_tokens = 250token_limit= 4096conversation=[]conversation.append(system_message)def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301"): encoding = tiktoken.encoding_for_model(model) num_tokens = 0 for message in messages: num_tokens += 4 # every message follows <im_start>{role/name}\n{content}<im_end>\n for key, value in message.items(): num_tokens += len(encoding.encode(value)) if key == "name": # if there's a name, the role is omitted num_tokens += -1 # role is always required and always 1 token num_tokens += 2 # every reply is primed with <im_start>assistant return num_tokenswhile(True): user_input = input("") conversation.append({"role": "user", "content": user_input}) conv_history_tokens = num_tokens_from_messages(conversation) while (conv_history_tokens+max_response_tokens >= token_limit): del conversation[1] conv_history_tokens = num_tokens_from_messages(conversation) response = openai.ChatCompletion.create( engine="gpt-35-turbo", # The deployment name you chose when you deployed the ChatGPT or GPT-4 model. messages = conversation, temperature=.7, max_tokens=max_response_tokens, ) conversation.append({"role": "assistant", "content": response['choices'][0]['message']['content']}) print("\n" + response['choices'][0]['message']['content'] + "\n")

In this example once the token count is reached the oldest messages in the conversation transcript will be removed. del is used instead of pop() for efficiency, and we start at index 1 so as to always preserve the system message and only remove user/assistant messages. Over time, this method of managing the conversation can cause the conversation quality to degrade as the model will gradually lose context of the earlier portions of the conversation.

An alternative approach is to limit the conversation duration to the max token length or a certain number of turns. Once the max token limit is reached and the model would lose context if you were to allow the conversation to continue, you can prompt the user that they need to begin a new conversation and clear the messages array to start a brand new conversation with the full token limit available.

The token counting portion of the code demonstrated previously, is a simplified version of one of OpenAI's cookbook examples.

Next steps

Working with the ChatGPT models

Important

Using GPT-35-Turbo models with the completion endpoint remains in preview. Due to the potential for changes to the underlying ChatML syntax, we strongly recommend using the Chat Completion API/endpoint. The Chat Completion API is the recommended method of interacting with the ChatGPT (gpt-35-turbo) models. The Chat Completion API is also the only way to access the GPT-4 models.

The following code snippet shows the most basic way to use the ChatGPT models with ChatML. If this is your first time using these models programmatically we recommend starting with our .

import osimport openaiopenai.api_type = "azure"openai.api_base = "https://{your-resource-name}.openai.azure.com/"openai.api_version = "2023-05-15"openai.api_key = os.getenv("OPENAI_API_KEY")response = openai.Completion.create( engine="gpt-35-turbo", # The deployment name you chose when you deployed the ChatGPT model prompt="<|im_start|>system\nAssistant is a large language model trained by OpenAI.\n<|im_end|>\n<|im_start|>user\nWho were the founders of Microsoft?\n<|im_end|>\n<|im_start|>assistant\n", temperature=0, max_tokens=500, top_p=0.5, stop=["<|im_end|>"])print(response['choices'][0]['text'])

Note

The following parameters aren't available with the gpt-35-turbo model: logprobs, best_of, and echo. If you set any of these parameters, you'll get an error.

(Video) Beginners Guide to GPT4 API & ChatGPT 3.5 Turbo API Tutorial

The <|im_end|> token indicates the end of a message. We recommend including <|im_end|> token as a stop sequence to ensure that the model stops generating text when it reaches the end of the message. You can read more about the special tokens in the Chat Markup Language (ChatML) section.

Consider setting max_tokens to a slightly higher value than normal such as 300 or 500. This ensures that the model doesn't stop generating text before it reaches the end of the message.

Model versioning

Note

gpt-35-turbo is equivalent to the gpt-3.5-turbo model from OpenAI.

Unlike previous GPT-3 and GPT-3.5 models, the gpt-35-turbo model as well as the gpt-4 and gpt-4-32k models will continue to be updated. When creating a deployment of these models, you'll also need to specify a model version.

Currently, only version 0301 is available for ChatGPT. We'll continue to make updated versions available in the future. You can find model deprecation times on our models page.

Working with Chat Markup Language (ChatML)

Note

OpenAI continues to improve the ChatGPT and the Chat Markup Language used with the models will continue to evolve in the future. We'll keep this document updated with the latest information.

OpenAI trained the ChatGPT on special tokens that delineate the different parts of the prompt. The prompt starts with a system message that is used to prime the model followed by a series of messages between the user and the assistant.

The format of a basic ChatML prompt is as follows:

<|im_start|>system Provide some context and/or instructions to the model.<|im_end|> <|im_start|>user The user’s message goes here<|im_end|> <|im_start|>assistant 

System message

The system message is included at the beginning of the prompt between the <|im_start|>system and <|im_end|> tokens. This message provides the initial instructions to the model. You can provide various information in the system message including:

  • A brief description of the assistant
  • Personality traits of the assistant
  • Instructions or rules you would like the assistant to follow
  • Data or information needed for the model, such as relevant questions from an FAQ

You can customize the system message for your use case or just include a basic system message. The system message is optional, but it's recommended to at least include a basic one to get the best results.

Messages

After the system message, you can include a series of messages between the user and the assistant. Each message should begin with the <|im_start|> token followed by the role (user or assistant) and end with the <|im_end|> token.

(Video) How to access Microsoft Azure OpenAI Services - GPT3.5 inside Azure Cloud

<|im_start|>userWhat is thermodynamics?<|im_end|>

To trigger a response from the model, the prompt should end with <|im_start|>assistant token indicating that it's the assistant's turn to respond. You can also include messages between the user and the assistant in the prompt as a way to do few shot learning.

Prompt examples

The following section shows examples of different styles of prompts that you could use with the ChatGPT and GPT-4 models. These examples are just a starting point, and you can experiment with different prompts to customize the behavior for your own use cases.

Basic example

If you want the ChatGPT and GPT-4 models to behave similarly to chat.openai.com, you can use a basic system message like "Assistant is a large language model trained by OpenAI."

<|im_start|>systemAssistant is a large language model trained by OpenAI.<|im_end|><|im_start|>userWho were the founders of Microsoft?<|im_end|><|im_start|>assistant

Example with instructions

For some scenarios, you may want to give additional instructions to the model to define guardrails for what the model is able to do.

<|im_start|>systemAssistant is an intelligent chatbot designed to help users answer their tax related questions. Instructions:- Only answer questions related to taxes. - If you're unsure of an answer, you can say "I don't know" or "I'm not sure" and recommend users go to the IRS website for more information.<|im_end|><|im_start|>userWhen are my taxes due?<|im_end|><|im_start|>assistant

Using data for grounding

You can also include relevant data or information in the system message to give the model extra context for the conversation. If you only need to include a small amount of information, you can hard code it in the system message. If you have a large amount of data that the model should be aware of, you can use embeddings or a product like Azure Cognitive Search to retrieve the most relevant information at query time.

<|im_start|>systemAssistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Serivce. Only answer questions using the context below and if you're not sure of an answer, you can say "I don't know".Context:- Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series.- Azure OpenAI Service gives customers advanced language AI with OpenAI GPT-3, Codex, and DALL-E models with the security and enterprise promise of Azure. Azure OpenAI co-develops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.- At Microsoft, we're committed to the advancement of AI driven by principles that put people first. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes requiring applicants to show well-defined use cases, incorporating Microsoft’s principles for responsible AI use<|im_end|><|im_start|>userWhat is Azure OpenAI Service?<|im_end|><|im_start|>assistant

Few shot learning with ChatML

You can also give few shot examples to the model. The approach for few shot learning has changed slightly because of the new prompt format. You can now include a series of messages between the user and the assistant in the prompt as few shot examples. These examples can be used to seed answers to common questions to prime the model or teach particular behaviors to the model.

This is only one example of how you can use few shot learning with ChatGPT. You can experiment with different approaches to see what works best for your use case.

<|im_start|>systemAssistant is an intelligent chatbot designed to help users answer their tax related questions. <|im_end|><|im_start|>userWhen do I need to file my taxes by?<|im_end|><|im_start|>assistantIn 2023, you will need to file your taxes by April 18th. The date falls after the usual April 15th deadline because April 15th falls on a Saturday in 2023. For more details, see https://www.irs.gov/filing/individuals/when-to-file<|im_end|><|im_start|>userHow can I check the status of my tax refund?<|im_end|><|im_start|>assistantYou can check the status of your tax refund by visiting https://www.irs.gov/refunds<|im_end|>

Using Chat Markup Language for non-chat scenarios

ChatML is designed to make multi-turn conversations easier to manage, but it also works well for non-chat scenarios.

For example, for an entity extraction scenario, you might use the following prompt:

<|im_start|>systemYou are an assistant designed to extract entities from text. Users will paste in a string of text and you will respond with entities you've extracted from the text as a JSON object. Here's an example of your output format:{ "name": "", "company": "", "phone_number": ""}<|im_end|><|im_start|>userHello. My name is Robert Smith. I’m calling from Contoso Insurance, Delaware. My colleague mentioned that you are interested in learning about our comprehensive benefits policy. Could you give me a call back at (555) 346-9322 when you get a chance so we can go over the benefits?<|im_end|><|im_start|>assistant

Preventing unsafe user inputs

It's important to add mitigations into your application to ensure safe use of the Chat Markup Language.

We recommend that you prevent end-users from being able to include special tokens in their input such as <|im_start|> and <|im_end|>. We also recommend that you include additional validation to ensure the prompts you're sending to the model are well formed and follow the Chat Markup Language format as described in this document.

You can also provide instructions in the system message to guide the model on how to respond to certain types of user inputs. For example, you can instruct the model to only reply to messages about a certain subject. You can also reinforce this behavior with few shot examples.

Managing conversations

The token limit for gpt-35-turbo is 4096 tokens. This limit includes the token count from both the prompt and completion. The number of tokens in the prompt combined with the value of the max_tokens parameter must stay under 4096 or you'll receive an error.

It’s your responsibility to ensure the prompt and completion falls within the token limit. This means that for longer conversations, you need to keep track of the token count and only send the model a prompt that falls within the token limit.

The following code sample shows a simple example of how you could keep track of the separate messages in the conversation.

import osimport openaiopenai.api_type = "azure"openai.api_base = "https://{your-resource-name}.openai.azure.com/" #This corresponds to your Azure OpenAI resource's endpoint valueopenai.api_version = "2023-05-15" openai.api_key = os.getenv("OPENAI_API_KEY")# defining a function to create the prompt from the system message and the conversation messagesdef create_prompt(system_message, messages): prompt = system_message for message in messages: prompt += f"\n<|im_start|>{message['sender']}\n{message['text']}\n<|im_end|>" prompt += "\n<|im_start|>assistant\n" return prompt# defining the user input and the system messageuser_input = "<your user input>" system_message = f"<|im_start|>system\n{'<your system message>'}\n<|im_end|>"# creating a list of messages to track the conversationmessages = [{"sender": "user", "text": user_input}]response = openai.Completion.create( engine="gpt-35-turbo", # The deployment name you chose when you deployed the ChatGPT model. prompt=create_prompt(system_message, messages), temperature=0.5, max_tokens=250, top_p=0.9, frequency_penalty=0, presence_penalty=0, stop=['<|im_end|>'])messages.append({"sender": "assistant", "text": response['choices'][0]['text']})print(response['choices'][0]['text'])

Staying under the token limit

The simplest approach to staying under the token limit is to remove the oldest messages in the conversation when you reach the token limit.

You can choose to always include as many tokens as possible while staying under the limit or you could always include a set number of previous messages assuming those messages stay within the limit. It's important to keep in mind that longer prompts take longer to generate a response and incur a higher cost than shorter prompts.

(Video) Can ChatGPT work with your enterprise data?

You can estimate the number of tokens in a string by using the tiktoken Python library as shown below.

import tiktoken cl100k_base = tiktoken.get_encoding("cl100k_base") enc = tiktoken.Encoding( name="gpt-35-turbo", pat_str=cl100k_base._pat_str, mergeable_ranks=cl100k_base._mergeable_ranks, special_tokens={ **cl100k_base._special_tokens, "<|im_start|>": 100264, "<|im_end|>": 100265 } ) tokens = enc.encode( "<|im_start|>user\nHello<|im_end|><|im_start|>assistant", allowed_special={"<|im_start|>", "<|im_end|>"} ) assert len(tokens) == 7 assert tokens == [100264, 882, 198, 9906, 100265, 100264, 78191]

Next steps

FAQs

Does ChatGPT use GPT-4? ›

The core service you pay for with ChatGPT Plus is access to GPT-4.

How are ChatGPT OpenAI and Azure OpenAI related? ›

ChatGPT is now available in preview on Microsoft's Azure OpenAI service allowing developers to integrate ChatGPT directly into a host of different enterprise and end-user applications using a token-based pricing system.

How do I access ChatGPT 4? ›

You can access GPT-4 playground via a subscription to OpenAI's ChatGPT Plus program, meaning access if limited to those who pay $20 per month. You can then access GPT-4 playground by choosing the chat mode in the 'Playground'. If you need to access the APIs in Playground, you'll need to join the waitlist.

How to use ChatGPT to generate code? ›

Using ChatGPT to generate code: How to use ChatGPT to generate Javascript code from the description of a problem or need.
  1. Identify a limit on Webflow.
  2. Describe your problem to Chat GPT.
  3. Ask him to give you the code to bypass this limit.
  4. Copy the code (and read its explanation, it's better 😉 )

How do I use ChatGPT in my application? ›

  1. Go to chat.openai.com to access the Chat GPT login page, and click on the “Sign up” button to get started.
  2. Fill out the registration form with your email address and password. ...
  3. You must verify your email address before you can get started. ...
  4. Once you verify your account, provide the required details.
Apr 29, 2023

What is difference between ChatGPT and GPT-4? ›

Unlike ChatGPT, which accepts only text, GPT-4 accepts prompts composed of both images and text, returning textual responses. As of the publishing of this article, unfortunately, the capacity for using image inputs is not yet available to the public.

Is GPT-4 better than ChatGPT? ›

ChatGPT got into hot water with a few strange responses to queries and one or two completely wrong answers. Fortunately, GPT-4 is more accurate than ChatGPT. OpenAI stated that GPT-4 is 82% less likely to respond to requests for content that OpenAI does not allow, and 60% less likely to invent answers.

How to access ChatGPT 4 for free? ›

Ora is a website that lets you use GPT-4 for free. Users will need to sign up for an account to use, and one can also use their existing Google account to sign in to Ora.ai. After signing up, it only takes a few steps to access GPT-4.

What is the difference between Microsoft AI and ChatGPT? ›

The biggest difference between Bing AI ChatGPT and OpenAI ChatGPT is that the version of Bing leverages the Prometheus technology to connect the chatbot with the Microsoft search engine to provide more accurate answers and offer responses to current events.

What is the difference between Azure OpenAI and ChatGPT? ›

GPT-3 is a general-purpose language model that can generate human-like text, while ChatGPT is specifically designed for conversational AI applications. Azure OpenAI offers a suite of language models for various applications, including GPT-3 and ChatGPT.

Does ChatGPT use CPU or GPU? ›

Chat GPT Hardware Requirements

A high-end CPU with at least 16 cores. At least 64 GB of RAM. A high-end GPU with at least 16 GB of VRAM. A large amount of storage space (at least several hundred GB)

How do I get API access to GPT-4? ›

Note on GPT-4 API Beta Access:

To gain access, you need to sign up for the waiting list. To do so, visit the OpenAI dashboard at https://platform.openai.com/ and navigate to the “Join the GPT-4 API waitlist” section. Click the “Sign Up” button and follow the instructions to join the waiting list.

Can I use ChatGPT for free? ›

Is ChatGPT free? Yes, you can use ChatGPT for free -- for now.

What is ChatGPT 4? ›

ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI and released in November 2022. The name "ChatGPT" combines "Chat", referring to its chatbot functionality, and "GPT", which stands for generative pre-trained transformer, a type of large language model (LLM).

Will ChatGPT 4 replace programmers? ›

FAQs About ChatGPT

No, GPT 4 will not replace programmers entirely. Like ChatGPT, it can potentially automate some aspects of programming, such as code generation and documentation. However, it cannot replace the human creativity and critical thinking required for complex software development.

How is ChatGPT so good at coding? ›

ChatGPT has the ability to understand code written in different languages, enabling developers to quickly work on projects without having to learn new syntax or writing styles. This saves time when coding complex algorithms or creating large programs from scratch.

How many lines of code is ChatGPT? ›

I see a very interesting future, where it will be possible to feed ChatGPT all 153 thousand lines of code and ask it to tell you what to fix. Microsoft (which owns Github) is already working on a "copilot" tool for Github to help programmers build code.

What is ChatGPT and how it works? ›

ChatGPT is an app built by OpenAI. Using the GPT language models, it can answer your questions, write copy, draft emails, hold a conversation, explain code in different programming languages, translate natural language to code, and more—or at least try to—all based on the natural language prompts you feed it.

Why is ChatGPT not working? ›

Reasons why Chat GPT may not be working include high traffic, corrupt browser cache or cookies, conflicting extensions, server issues, and internet connection problems. To fix Chat GPT not working, users can first check the Chat GPT server status to ensure that it is not down.

How to use ChatGPT without login? ›

4 Ways to Use ChatGPT Without an OpenAI Account
  1. Bing Chat. Bing Chat is probably the easiest way for most people to use ChatGPT without an OpenAI account. ...
  2. Bing Mobile App. 3 Images. ...
  3. Merlin. Merlin is a browser extension that's available for Google Chrome and Microsoft Edge. ...
  4. ChatGPT Writer.
May 5, 2023

What can you do with ChatGPT 4? ›

Here are some ways that developers can harness the power of GPT-4 to unlock its full potential.
  • 3D Design. ...
  • Mini-games creation. ...
  • Code debugging. ...
  • Finding vulnerabilities in security. ...
  • Creating extensions. ...
  • Sketches into website. ...
  • Copilot Microsoft Excel. ...
  • Learning a language.
Apr 11, 2023

What is so special about ChatGPT 4? ›

Chat GPT-4 is meant to give users substantially more accurate responses to their queries. According to OpenAI's update announcement, GPT-4 is "40% more likely to produce factual responses than GPT-3.5." GPT-4 also has more advanced reasoning capabilities when compared to ChatGPT-3.5.

Can ChatGPT 4 generate images? ›

In short, no. Chat GPT does not have the ability to generate images or draw pictures. The AI bot was not designed to produce any artwork but instead output text.

What are the limitations of GPT-4? ›

Firstly, you cannot fine-tune the GPT-4 models, and GPT-4 doesn't update its knowledge in real time. It was trained with the data up to September, 2021, so it has no knowledge of anything that has happened since. And finally, sometimes it makes up facts.

Is ChatGPT 4 up to date 2023? ›

It was released on March 14, 2023, and has been made publicly available in a limited form via the chatbot product ChatGPT Plus (a premium version of ChatGPT), and with access to the GPT-4 based version of OpenAI's API being provided via a waitlist.

How many parameters are there in chat GPT-4? ›

Chatgpt 4 parameters are while the latter is represented by an incomparably larger circle, suggesting an enormous count of 100 trillion parameters.

How much does chatgpt4 cost? ›

There are two pricing options available for GPT-4, starting at $0.03 for 1K prompt tokens. However, if you are accessing GPT-4 in ChatGPT Plus, then you need to subscribe to its monthly plan, which costs $20/month.

Which apps use chat GPT-4? ›

Top 5 Chat GPT Apps for iOS and Android
  • Chat AI: Ask & Write Anything – The Comprehensive Chat GPT App for iOS and Android. ...
  • 2.AI Chat Plus PRO with GPT – The AI Assistant for Busy Professionals. ...
  • The best Chat GPT apps are: ...
  • Genie – AI Chatbot – The Creative's AI Companion.
Mar 19, 2023

Which AI is better than ChatGPT? ›

16 Best ChatGPT Alternatives in 2023 (Free and Paid)
  • Microsoft Bing.
  • Perplexity AI.
  • Google Bard AI.
  • Jasper Chat.
  • Chatsonic.
  • Pi, your personal AI.
  • GitHub Copilot X.
  • Amazon Codewhisperer.
4 days ago

Is ChatGPT the most advanced AI? ›

OpenAI, a leading research organization in the field of artificial intelligence (AI), has recently released Chat GPT-4, the latest iteration of their language model. This release has generated a lot of excitement and anticipation, as it is the most advanced and powerful AI yet.

Is there a limit on ChatGPT usage? ›

The default length is fixed at 2048 tokens, while the maximum can be set at 4096 tokens. Restricting the token usage can result in short answers, which might limit the output and mar your usage experience.

Which OpenAI model should I use? ›

Davinci is the most capable model and can perform any task the other models can perform, often with less instruction. For applications requiring deep understanding of the content, like summarization for a specific audience and creative content generation, Davinci produces the best results.

Which cloud does ChatGPT run on? ›

OpenAI and ChatGPT use Microsoft Azure's cloud infrastructure to deliver the performance and scale necessary to run their artificial intelligence (AI) training and inference workloads.

Is ChatGPT using Azure? ›

ChatGPT is now available in Microsoft's Azure OpenAI service.

How many GPUs are needed to run ChatGPT? ›

ChatGPT will require as many as 30,000 NVIDIA GPUs to operate, according to a report by research firm TrendForce.

What hardware is ChatGPT trained on? ›

The most popular deep learning workload of late is ChatGPT, in beta from Open.AI, which was trained on Nvidia GPUs.

What processors does ChatGPT run on? ›

The new VMs are powered by Nvidia H100 Tensor Core GPUs ("Hopper” generation) interconnected via next gen NVSwitch and NVLink 4.0, Nvidia's 400 Gb/s Quantum-2 CX7 InfiniBand networking, 4th Gen Intel Xeon Scalable processors (“Sapphire Rapids”) with PCIe Gen5 interconnects and DDR5 memory.

Is the GPT-4 API free? ›

Open AI recently unveiled its latest AI model, GPT-4, which can process both text and image inputs to generate various types of responses. However, the GPT-4 API is not publicly available and can only be accessed by a limited number of individuals who pay a monthly fee of $20.

What is the difference between ChatGPT 3 and 4? ›

Chat GPT – 3 vs Chat GPT – 4

Text production more closely resembles human behavior, and speed patterns have improved GPT-4, which promises a significant performance gain over GPT-3. GPT-4 is more flexible and adaptable when handling language translation and text summarization tasks.

Can GPT-4 access the Internet? ›

No, ChatGPT 4 is not connected to the internet nor does it have any connection to external data sources. However, ChatGPT does have a number of other Artificial intelligence platforms that do contain internet access such as Google AI chatbot Bard AI which contains GPT-4 and has internet access available.

Is ChatGPT better than Google? ›

While both ChatGPT and Google have their own unique capabilities, they are used for different purposes. ChatGPT is a sophisticated AI chatbot that is capable of understanding and responding to natural language, while Google is a powerful search engine that is used for finding specific information on the Internet.

How to generate text with gpt2? ›

  1. Step 1: Determine input prompt and visualize word dependencies. GPT-2 uses input text to set the initial context for further text generation. ...
  2. Step 2: Use an ML model to generate text based on prompt. ...
  3. Step 3: Explore use cases and model parameters. ...
  4. Step 4: Use Amazon SageMaker batch transform. ...
  5. Step 5: Next steps.

How does GPT-3 generate text? ›

GPT-3, or the third-generation Generative Pre-trained Transformer, is a neural network machine learning model trained using internet data to generate any type of text. Developed by OpenAI, it requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text.

Can I chat with GPT-3 AI? ›

Can I chat with GPT-3 AI? Yes, you can chat with GPT-3 AI. The chatbot built with GPT-3 AI can understand and generate human-like responses to your queries.

Does ChatGPT run on Azure? ›

ChatGPT is now available in Azure OpenAI Service.

How much text is chat GPT trained on? ›

To be even more exact, 300 billion words were fed into the system. As a language model, it works on probability, able to guess what the next word should be in a sentence.

How to use pretrained GPT-2? ›

Tutorial Overview
  1. Step 1: Install Library.
  2. Step 2: Import Library.
  3. Step 3: Build Text Generation Pipeline.
  4. Step 4: Define the Text to Start Generating From.
  5. Step 5: Start Generating.
  6. BONUS: Generate Text in any Language.
Jan 19, 2021

Is GPT-2 an encoder or decoder? ›

Not just GPT-3, the previous versions, GPT and GPT-2, too, utilised a decoder only architecture. The original Transformer model is made of both encoder and decoder, where each forms a separate stack.

What is the difference between ChatGPT and OpenAI? ›

Both Chat GPT and OpenAI Playground are designed to make language generation easier and more accessible to a wider audience. While Chat GPT is more focused on providing a quick and easy way to generate text, OpenAI Playground is designed to offer more advanced customization options and experimentation tools.

What is the difference between GPT-3 and ChatGPT? ›

ChatGPT is an app; GPT is the brain behind that app

ChatGPT is a web app (you can access it in your browser) designed specifically for chatbot applications—and optimized for dialogue. It relies on GPT to produce text, like explaining code or writing poems. GPT, on the other hand, is a language model, not an app.

How does ChatGPT actually work? ›

Chat GPT uses deep learning algorithms to analyze input text prompts and generate responses based on patterns in the data it has been trained on. It is trained on a massive corpus of text, including books, articles, and websites, allowing it to understand language nuances and produce high-quality responses.

Videos

1. How to Use GPT 4 Free (without ChatGPT Plus)
(Tim Harris Video)
2. Intro to Azure OpenAI & ChatGPT (gpt-35-turbo)
(Tinkering with Deep Learning & AI)
3. Enterprise ChatGPT with Azure OpenAI & Azure Cognitive Search
(Jon Gallant)
4. Making Enterprise GPT Real with Azure Cognitive Search and Azure OpenAI Service
(Microsoft Developer)
5. What is GPT4 and How You Can Use OpenAI GPT 4
(Adrian Twarog)
6. Supercharging your Apps with GPT and Azure OpenAI Service
(Microsoft Reactor)
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