OpenAI has altered the large language model scene by lowering costs on their GPT-3.5 Turbo model. The 50% reduction in input costs and a 25% drop in output costs make the model significantly more accessible for developers and hobbyists. This could make powerful language processing available to more people and also put pressure on competitors to lower their prices.
This article talks about why the GPT-3.5-Turbo Model is better than other models, like the GPT-4. We will talk about the new use cases and look at the changes that have been made. We will also give you code snippets to show you how to implement it. Looking for the best AI design tools?Take the guesswork out of finding the best of the best when the Service List has it all to help you narrow down your choices.” and link to servicelist.io, and anchor text “AI services reviews“
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What is the GPT-3.5-Turbo Model?
The GPT-3.5-Turbo Model remains a sturdy and less expensive preference, even though the GPT-4 Model is likewise to be had. It is accountable for walking the famous ChatGPT, and users can create their own chatbots with similar capabilities.
One of the best things about the GPT-3.5-Turbo model is that it can handle multiple turns, which means it can take in a number of messages. The GPT-3 model only supported single-turn text prompts, so this feature is an improvement. Users can use pre-set scenarios and previous responses as context to improve the quality of the response that is generated with this feature. We’ll talk more about these features in a later section.
Comparison between GPT-3 vs GPT-3.5-Turbo vs GPT-4
The GPT-4 model produces better results, but the GPT-3.5-Turbo model is a much more affordable choice. It gets results that are good enough, about the same as ChatGPT’s. It also has faster API responses and the same mode for multi-turn chat completion.
Compared to the GPT-3.5-Turbo, the GPT-3 model is less appealing unless it is fine-tuned because it costs more and produces worse results. The GPT-3.5-Turbo type seems to be the best choice most of the time. You can always switch to GPT-4 if you want to. It uses the same API calls as GPT-4.
We will see the difference between the old prompt-style question and the new message-style query used by the GPT-3.5-Turbo Model in the next bit. To make the conversation more useful, the new model lets us add more context and even past responses.
How to Use the GPT-3.5-Turbo Model
The process of upgrading to the new GPT-3.5-Turbo Model API is easy, and this guide will show you how to do it with Node.js. This idea can be used with other programming languages, though. There is code in the lesson that demonstrates how to use both the old GPT-3 method and the brand-new GPT-3.5-Turbo method.
Make sure you have your OpenAI API key and have set up your project correctly before moving on.
- You can set the GPT35TurboMessage variable to a list of objects that act out a conversation between a user, a helper, and a system. This list will be passed into the GPT-3.5-Turbo model.
- The GPT-3 and GPT-3.5-Turbo models can be used to produce text through two functions, named GPT3 and GPT35-Turbo.
- The GPT3 function uses the openai.createCompletion() method to generate text using the GPT3Prompt variable and the text-davinci-003 GPT-3 model.
- The last step is to use console.log() to record the text that the GPT-3 and GPT-3.5-Turbo units produce.
The GPT-3.5-Turbo model gives better results than the GPT-3 model because it gives more context to requests, which makes it easier to customize message content and better task fit with fewer changes needed.
Best Practices for Using the GPT-3.5-Turbo Model
- Use the newest type at all times.
- Conversations with more than one person generally go better.
- Messages from the system can help set up the behavior you want.
- The helper message and the user message both give more information.
- In the request, say what format you want the result to be in.
- The models don’t remember past requests, so when you send a message, include all the relevant background information.
- All of the other factors, like temperature and max_tokens, still work as they did before.
- Like ChatGPT, the model API can act up at times, so I suggest adding an “auto delay and retry” wrapper to your API calls to deal with errors like server overload and the maximum token limit.
Some Thoughts on Potential Use Cases
AI is changing quickly, getting cheaper, and giving medium- to large-sized businesses more options. However, trying to replace all workers with AI might not be well understood. Memory has a limit on how much data it can store and process.
AI should be seen as a way to help people do their jobs better, not as a replacement for them. Think about how AI could be useful in different situations.
Customer Support
The quality of service, employee productivity, and management satisfaction can all be improved by using AI in customer service. Customized chatbots can efficiently manage text-based queries, while AI tools like OpenAI’s Whisper can address voice-based queries.
Text-to-speech services, like Amazon Polly, can improve the customer experience by turning voice notes into text and answering questions that are asked over the phone. This can save time and money, which will make the customer happier in the end.
Legal Service
Law firms can benefit a lot from AI if it handles initial consultations and looks over complicated legal documents. AI-powered chatbots can answer simple questions, freeing up staff to deal with more complicated problems. Models that use natural language processing, like GPT models, can quickly look over thousands of lines of text.
Wrap Up
AI technology can benefit businesses, with increasing availability and affordability leading to more companies offering tailored services to businesses of all sizes.
