How to Build a Product on top of GPT-3?

When artificial intelligence is transforming the landscapes of many business sectors, the relevance of language models like GPT-3 is getting more important than ever. With the advent of powerful language models, it will be only a matter of time before business tools enabled by artificial intelligence achieve mass adoption.

But with everyone having access to the same language-generating tools, how do you get a competitive edge? How can businesses leverage these cutting-edge technologies to automate at scale without sacrificing content production quality? In this article, you will find everything you need to know about building a product on top of GPT-3.

• What is GPT-3?
• How to build a product on top of GPT-3
• Security Considerations while building your product
• Benefits of using building a product on top of GPT-3

What is GPT-3?

GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language processing model developed by OpenAI. It is trained on a massive amount of text data and can generate human-like text, perform natural language understanding tasks and answer questions. GPT-3 can be fine-tuned for a wide range of natural language processing tasks such as language translation, question answering, summarization, and more. The model has 175 billion parameters, which is significantly more than its predecessor GPT-2, making it one of the largest models of its kind. Because of its ability to understand and generate human-like text, GPT-3 has the potential to be used in many applications such as chatbots, virtual assistants, content generation, and more. Here is an article to learn more on how to get started with GPT3

Building a Product with GPT-3

 Building successful GPT-3 products presents unique challenges, as the model has a lot of capabilities, and finding the right use case that aligns with the needs of the end users is crucial.  Here are a few tips to help you leverage the power of GPT-3 while keeping your operations scalable and cost-efficient:

The Process of Ideation

Starting with an idea is crucial when building a product with GPT-3. It helps if you know whether you plan to build a completely new product or integrate GPT-3 into an existing one. It is also very important to review your idea against the OpenAI Use Case Guidelines to ensure that it is aligned with OpenAI’s policies and that you save time and effort on building something that will not be approved for public release. As you mentioned, OpenAI currently prohibits using its API for certain types of products, such as those that provide medical or legal advice, generate political content, or replicate the functionality of the OpenAI API without proper authorization.

It’s also important to remember that technology and policies are constantly evolving, so it’s a good idea to stay informed about the latest developments and adjust your product accordingly. Additionally, it’s important to be transparent with your users about the model’s limitations and to provide a disclaimer where necessary. By following these guidelines, you can ensure that your product is aligned with OpenAI’s policies and can be successfully launched in the market.

Mandatory API Access

Apply for API access to OpenAI as early as possible, even if your product idea is high-level. This will help you secure a spot on the waitlist and ensure you have access to the API when you are ready to start building your product.

You will need to provide basic information about your proposed use case and how you plan to use the API during the application process. You do not need to provide detailed market research or a fully developed product plan. As long as your proposed use case aligns with OpenAI’s guidelines, you can continue to refine and develop your idea as you wait for API access.

It’s worth noting that OpenAI is selective in granting API access and that there may be a waitlist. Applying as early as possible will increase your chances of being approved for API access when needed. Once you get access, it is important to ensure that your product implementation aligns with the use case you described in your API access application and follows OpenAI’s guidelines. Keep in mind that API access is subject to ongoing review and that OpenAI may revoke access if you are found to violate their policies.

GPT-3 API Prompt Engineering

Prompt engineering is a crucial step when building a product with GPT-3. It involves crafting the right prompts to input into the API to get the desired output. A good approach when building a product with GPT-3 is to start by designing your prompts in the API playground. This allows you to experiment with different prompts and parameters to find the best combination for your idea. The API playground allows you to input a prompt and see the generated output, and you can adjust the settings, such as the number of responses and temperature, and fine-tune the output. Once satisfied with the output, you can export the prompt and the settings and use them in your code. The playground also allows you to export the code in python or CURL, which makes it easy to integrate the prompt into your product. Once you have the desired prompt and parameters combination, you can start integrating it into your code. You can use the exported code to make API calls from your application and process the generated text to build your product.

You will also have access to the OpenAI API playground when granted API access. The API playground is a web-based tool that allows you to test the capabilities of GPT-3 without writing any code. You can input a prompt, and the API will generate text based on that prompt. The API playground is a useful tool for testing out different prompts and experimenting with the capabilities of GPT-3 before you start building your product.

The API playground allows you to:

1. Test different prompts: You can test different prompts and see how GPT-3 responds to them. This can help you fine-tune your prompts to get the best results.
2. Experiment with different models: You can experiment with different models and compare the results. You can test models with different numbers of parameters and fine-tune them to your specific use case.
3. Test the API calls: You can test the API calls and check the API’s response time and limits.
4. Try out the different models’ settings: You can try different settings for a specific model, such as temperature and top-p, to see how it affects the output.
5. Get a sense of the quality of the output: you can use the API playground to evaluate the quality of the generated text and fine-tune your prompts and settings accordingly.

The API playground is a powerful tool for testing and experimenting with GPT-3 before building your product. It can save you time and resources in the long run.

Product Development

OpenAI provides an official Python library for accessing the API, but it’s also possible to make API calls using other programming languages, such as CURL. This means it’s possible to build an OpenAI-enabled product using various tech stacks, such as coding stacks built around languages like Python, JavaScript, Ruby, and others. Additionally, it’s also possible to use low-code or no-code tools like Vercel, Supabase, Bubble, and Webflow to build a product with GPT-3.

You need an API key to call the GPT-3 API from your product. You can get an API key by signing up for an OpenAI API key. Once you have the API key, you can make API calls using the API endpoint provided by OpenAI. You can use any programming language that can send HTTP requests to make API calls. An example code is shown below; This code below will return a JSON object containing the completion generated by the GPT-3 model.

import requests

url = 'https://api.openai.com/v1/engines/davinci-codex/completions'

api_key = '<YOUR_API_KEY>'

prompt = 'What is the capital of France?'

response = requests.post(
    url,
    json={
        'prompt': prompt,
        'max_tokens': 50,
        'stop': '\n',
        'model': 'davinci-codex'
    },
    headers={
        'Content-Type': 'application/json',
        'Authorization': f'Bearer {api_key}'
    }
)

print(response.json())

API cost is one of the most important factors when building an OpenAI-enabled product. As GPT-3 is a powerful and resource-intensive model, the cost of using the API can significantly impact your product development decisions. Understanding the cost of using the API and how it fits into your overall business model is important before you build your product.

OpenAI Approval Before Going Live

Before you launch your product, it’s important to obtain approval from OpenAI. OpenAI has strict guidelines for the use of its API, and it’s important to ensure that your product complies with these guidelines before going live. 

By obtaining approval from OpenAI before going live, you can ensure that your product complies with OpenAI’s guidelines and is legally and ethically sound. This will help you avoid any issues or problems arising after launching your product.

Security Considerations while Building your Product

Security is an important consideration when building a product with OpenAI GPT-3. Here are a few things to keep in mind:

1. Secure access to the API: Use secure methods to access the OpenAI API, such as using an API key and implementing authentication and authorization mechanisms.
2. Protect user data: Ensure user data, such as prompts and generated text, is stored securely and not shared with unauthorized parties.
3. Encryption: Use encryption for data in transit and at rest to protect user data and communications with the API.
4. Monitor for suspicious activity: Regularly monitor for suspicious activity and take appropriate action, such as revoking access, if necessary.
5. Continuously evaluate and improve security: Keep up to date with the latest security best practices and continuously evaluate and improve the security of your product.
6. Compliance: Be aware of and comply with relevant regulations and standards, such as GDPR or HIPAA, if necessary.

By keeping these security considerations in mind, you can help ensure that your product is secure and user data is protected. It’s important to keep security in mind throughout the development process and continuously evaluate and improve your product’s security.

Benefits of Building a Product on top of GPT-3

Building a product on top of GPT-3 can provide many benefits, some of which include the following:

Advanced natural language processing capabilities:  Building a product on top of GPT-3 is its advanced natural language processing capabilities. GPT-3 is a powerful language model trained on massive text data, allowing it to understand and generate natural text similar to human language. This makes it well-suited for building products that require advanced natural language processing capabilities.

Improved user experience: GPT-3 can also provide an improved user experience. Because GPT-3 is a powerful language model that can understand and generate natural language text similar to human language, products built with GPT-3 can provide a more natural and intuitive user experience and customer retention. For example, products built with GPT-3 can understand text’s meaning and context, allowing it to understand the user’s intent and provide a more accurate response. This can be used to build products that provide natural language understanding and conversation capabilities.

Automation of repetitive tasks: Using GPT-3, users can implement intelligent process automation to automate repetitive tasks such as content generation, data analysis, and customer service that would otherwise require human labor, which can save time and money.

Increased efficiency: GPT-3 is trained on a massive amount of text data, which can be used to build products that can access and analyze large amounts of data. So, this tool can be used to increase efficiency and productivity while reducing human error by automating tasks prone to errors when done manually.

Personalization: Building a product on top of GPT-3 can also provide personalization capabilities. GPT-3 can understand the meaning and context of a text, which can be used to understand user preferences and provide tailored recommendations or suggestions that enhance user satisfaction

Closing Thoughts

In addition to these benefits, businesses can build their product on top of GPT-3 to create virtual assistants to handle customer inquiries and provide helpful information. It can also be used for automated writing, such as for creating emails, documents, and other text-based content. Businesses can also use the tool for language translation to enable multilingual customer service or for international expansion and text summarization, which can be useful for summarizing news articles or long documents. Overall, GPT-3 can be used for multiple use cases and can help businesses to improve their products, automate tasks and increase efficiency, productivity, revenue, and customer satisfaction.