Open chat ai, if you are referring OpenAI chat then it is based on Large language models (LLMs) which are deep learning algorithms that can recognise, summarise, translate, predict, and generate content using very large datasets. lets understand it one by one,
Open ai chatGPT models
The models in the GPT series are generally referred to by the version number, such as GPT-2 ,GPT-3 and GPT-4. The GPT-4 model is the latest and most advanced version in the series.
Here’s a brief overview of the key models in the GPT series:
1.GPT-1: The first model in the series, GPT-1, had 117 million parameters. It was a breakthrough in natural language processing, demonstrating the effectiveness of large-scale transformer models.
2.GPT-2: GPT-2 was a significant advancement, featuring 1.5 billion parameters, making it substantially larger than its predecessor. It gained attention for its ability to generate coherent and contextually relevant text, sometimes indistinguishable from human writing.
3.GPT-3: GPT-3 is the most recent and largest model in the GPT series, with a staggering 175 billion parameters. It has demonstrated remarkable capabilities in various natural language understanding tasks, including translation, question-answering, and text completion. GPT-3 is known for its ability to generate high-quality responses in a conversational context.
As for “ChatGPT,” it’s a term that is informally used to describe the application of GPT models in chat-based interactions. GPT-3, with its large parameter count and improved capabilities, has been particularly well-suited for chat-based applications. It allows users to input prompts or messages and receive coherent and contextually relevant responses, making it suitable for chatbots, virtual assistants, and other conversational AI applications.
How to use openAI chatGPT
Prompt engineering
To get better results from large language models (sometimes referred to as GPT models) like GPT-4 or GPT3. The methods described here can sometimes be deployed in combination for greater effect. We encourage experimentation to find the methods that work best for you.
Some of the examples demonstrated here currently work only with our most capable model, gpt-4. In general, if you find that a model fails at a task and a more capable model is available, it’s often worth trying again with the more capable model.
Six strategies for getting better results from chatGPT
Write clear instructions
These models can’t read your mind. If outputs are too long, ask for brief replies. If outputs are too simple, ask for expert-level writing. If you dislike the format, demonstrate the format you’d like to see. The less the model has to guess at what you want, the more likely you’ll get it.
| How do I add numbers in Excel? | How do I add up a row of dollar amounts in Excel? I want to do this automatically for a whole sheet of rows with all the totals ending up on the right in a column called “Total”. |
| Write code to calculate the Fibonacci sequence. | Write a TypeScript function to efficiently calculate the Fibonacci sequence. Comment the code liberally to explain what each piece does and why it’s written that way. |
Use delimiters to clearly indicate distinct parts of the input
Delimiters like triple quotation marks, XML tags, section titles, etc. can help demarcate sections of text to be treated differently.
Specify the steps required to complete a task
Some tasks are best specified as a sequence of steps. Writing the steps out explicitly can make it easier for the model to follow them.
Provide examples
Providing general instructions that apply to all examples is generally more efficient than demonstrating all permutations of a task by example, but in some cases providing examples may be easier. For example, if you intend for the model to copy a particular style of responding to user queries which is difficult to describe explicitly. This is known as “few-shot” prompting.
Specify the desired length of the output
You can ask the model to produce outputs that are of a given target length. The targeted output length can be specified in terms of the count of words, sentences, paragraphs, bullet points, etc. Note however that instructing the model to generate a specific number of words does not work with high precision. The model can more reliably generate outputs with a specific number of paragraphs or bullet points.
Provide reference text
Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs. In the same way that a sheet of notes can help a student do better on a test, providing reference text to these models can help in answering with fewer fabrications.
Instruct the model to answer using a reference text
- Instruct the model to answer with citations from a reference text
Split complex tasks into simpler subtasks
Just as it is good practice in software engineering to decompose a complex system into a set of modular components, the same is true of tasks submitted to a language model. Complex tasks tend to have higher error rates than simpler tasks. Furthermore, complex tasks can often be re-defined as a workflow of simpler tasks in which the outputs of earlier tasks are used to construct the inputs to later tasks.
- Use intent classification to identify the most relevant instructions for a user query
- For dialogue applications that require very long conversations, summarise or filter previous dialogue
- Summarise long documents piecewise and construct a full summary recursively
Can we develop Plugins for chatGPT
OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT’s capabilities and allowing it to perform a wide range of actions. Plugins enable ChatGPT to do things like:
- Retrieve real-time information; e.g., sports scores, stock prices, the latest news, etc.
- Retrieve knowledge-base information; e.g., company docs, personal notes, etc.
- Assist users with actions; e.g., booking a flight, ordering food, etc.