OpenAI ChatGPT API review

OpenAI API provides access to powerful machine learning models. The API allows developers to use these models for a variety of text processing tasks, generating text and graphics content, and data analysis.

API features and functionality

The OpenAI API offers a variety of capabilities, including image generation, audio transcription, text generation, text translation, classification and sentiment analysis, and the creation of tools to automatically answer user questions.

Examples of API usage scenarios

Generation of text and graphic content for blogs and websites based on specified parameters and styles
Creating chatbots or virtual assistants
Example “How to build an AI that can answer questions about your website” https://platform.openai.com/docs/tutorials/web-qa-embeddings
Automatic translation and adaptation of text into various languages, text stylization
Analysis and classification of text data to determine sentiment or categorize content
Creating tools for generating thematic, specialized, targeted content and automation

Practical tips for using the OpenAI API effectively

Use the appropriate model for each specific task to achieve the best results.
Experiment with options and settings to customize the generation or analysis to suit your needs.
Consider API limitations and ethical issues when developing and using applications.

Easy integration

Integrating the OpenAI API into web development is relatively simple. You must register on the OpenAI platform, obtain an API key and use it to send requests and receive results.

For integration into PHP projects, there are several SDK libraries from the OpenAI community that make it easier to integrate APIs into your web applications. (the most popular - https://github.com/openai-php/client)

Available models

The OpenAI API operates on a variety of models with varying capabilities and costs. You can also customize the base models to a limited extent to suit your specific use cases using fine tuning.

GPT-4 Limited (beta) - A set of models that improve GPT-3.5 and are capable of understanding and generating natural language or code.
GPT-4 is currently in limited beta and is only available to those granted access.
GPT-3.5 - A set of models that improve GPT-3 and are capable of understanding and generating natural language or code
DALL·E (beta) - Model capable of generating and editing images based on text description
Whisper (beta) - Model capable of converting audio to text
Embeddings - A set of models capable of converting text into numerical form
Moderation - A fine-tuned model capable of detecting possibly sensitive or unsafe text
GPT-3 - A set of models capable of understanding and generating natural language.
These include the Ada, Babbage, Curie, Davinci models, which are also used for InstructGPT and Fine- endpoints tuning
Codex (Deprecated) A set of models capable of understanding and generating code, including translation of natural language into code

For GPT-4 and GPT-3.5 models, two operating modes are available: Chat Completions API and Completions API. In Chat Completions API mode, the API transmits a set of messages, including conversations with specified roles, to preserve context. In Completions API mode, only a single prompt line is transmitted. In both cases, the models have a limit on the number of tokens.

An important parameter is the size of the context, which consists of request and response tokens. If your request contains too many tokens to fit within the model's maximum limit (for example, more than 4096 tokens for gpt-3.5-turbo), you will have to shorten or change the text to fit. Note that if a message is removed from the input, the model will lose all information about it, so be careful when trimming the context.

Fine Tuning

Fine tuning — this is a powerful method for creating a new model that fits your use case. Once your model is fine-tuned, you no longer need to provide examples in the tooltip. This reduces costs and allows queries to be executed with lower latency.

Fine tuning includes the following steps:

Preparing and loading training data Training data — this is how you teach GPT-3 what you want it to say. (You should provide at least several hundred high quality examples). Your data should be a JSONL document, where each line is a hint-completion pair corresponding to the training example.
Train a new, fine-tuned model. Additional training is also possible at a later date.
Use your modified model

Fine tuning is currently only available for the following base models: davinci, curie, babbage, and ada. These are original models that do not have any instructions after training.

Creating your own model is especially important if you need to operate with data that is not were publicly available until September 2021, as well as when it is necessary to increase the accuracy of answers based on your own knowledge base and take into account certain specifics when generating an answer.

Cost

Access to the OpenAI API is paid. Payment is made based on tokens used for text, minutes for audio, and units for images. The cost of the token depends on the chosen model. For example, the most advanced model to date, GPT-4 with 32K context, has a cost of approximately $0.12 per thousand tokens, which is approximately equivalent to 750 words in English. At the same time, the gpt-3.5-turbo model, which is sufficient for most tasks, has a significantly lower cost - $0.002 per 1 thousand tokens. It is important to consider that the cost calculation takes into account not only the response text, but also the input request (prompt).

Tariff plans - https://openai.com/pricing#language-models

The cost effectiveness of OpenAI API integration depends on the specific needs and goals of your project. It is necessary to evaluate the potential benefits of using the API and weigh the costs of integration and use.

OpenAI API features

For working with text

OpenAI API provides a rich set of capabilities for working with text using powerful deep learning models. The following are some of the main features of the OpenAI API for working with text data:

Text generation: Using the OpenAI API, you can generate new text based on given inputs or descriptions. This can be useful for automatically generating content for blogs, articles, advertisements and other text elements.
Auto-completion and auto-correction of text: The OpenAI API can be used to automatically complete or correct text. This can be useful for creating autocomplete functionality in search strings or text editors.
Text translation: The API allows you to translate text from one language to another. You can use the OpenAI API to create tools and applications that provide a translator that supports multiple languages.
Text Analysis and Classification: The OpenAI API provides the ability to analyze text and perform text classification. This can be useful for determining the sentiment of text, highlighting keywords or topics, classifying reviews, and other tasks related to natural language processing and analysis.
Code and technical documentation generation: Using the OpenAI API, you can generate code based on specifications and descriptions. This can be useful for automatically generating code for web development, creating technical documentation, or implementing autocompletion features in development environments.
Chatbots and automatic answering of questions: OpenAI API allows you to create chatbots and automatic answering systems for user questions. This can be useful for developing virtual assistants, customer support systems or automated communication systems.
Natural language analysis and processing: The OpenAI API provides capabilities for natural language analysis and processing, including semantic analysis, parsing, named entity recognition, and other tasks related to processing text data.</li >

It is important to note that the OpenAI API provides extensive capabilities for working with text, but also has its limitations, including limits on the number of requests and text size. It is also worth remembering the rules for using the OpenAI API and ethical issues when creating applications based on AI and text data processing.

For working with graphics

Image generation: Using the OpenAI API, you can create new images based on specified parameters or descriptions. For example, you can use the API to generate unique illustrations, designs, photorealistic images and other graphic elements.

Image editing and modification: The OpenAI API allows you to make changes to existing images. This may include changing the color palette, adding or removing objects, modifying textures and styles, and other graphic manipulations.

For working with audio

Using the OpenAI API, you can transcribe audio files, converting speech into text. It is also possible to automatically translate audio transcription into English.

Possible problems and difficulties during integration

When integrating and using the OpenAI API, there may be some issues and complications that should be considered. Below are some of them:

Restrictions and limits: The OpenAI API has its limitations and limits, including a limit on the number of requests in a certain time period and a maximum size of input data. This may limit your ability to use the API in some cases, especially if you need to process large amounts of data or use the API intensively.
Model training and tuning complexity: Achieving optimal results with the OpenAI API may require model training or parameter tuning. This requires some knowledge of machine learning and experience working with AI models. Improper setup or training of the model may result in undesirable results or poor output quality.
Data Security and Privacy: When working with the OpenAI API, it is important to ensure the security and privacy of the data being processed. Incorrect processing or storage of data may lead to leakage of confidential information or violation of user privacy. Therefore, it is necessary to take appropriate measures to protect data and follow security recommendations.
Quality and reliability of results: Results obtained using the OpenAI API may vary depending on the complexity of the problem, data quality, and model settings. Sometimes the results may be unsatisfactory or require additional processing. It is important to test and validate your API output to ensure it meets your requirements and expectations.
Cost of Use: The OpenAI API is a paid service, and the cost of its use can be a significant factor in integration and development. When planning to use the OpenAI API, you must consider its plans, pricing, and evaluate the cost-effectiveness of API integration in relation to the expected benefits and results.

A note about the importance of prompt engineering

Proper formulation of statements is an important aspect when working with language models, since it directly affects the generated results and the behavior of the model. Here are some tips for correctly formulating promts:

Output Style Control: Prompts can be used to direct the model to adopt a particular style or tone in its responses. This is especially useful when you want the generated text to fit a specific context or adhere to a specific writing style.
Clarify instructions for complex problems: When working with complex problems, detailed instructions can help ensure that the model correctly understands and follows the instructions. This is especially true when using instructional models such as InstructGPT.
Including details in your query to generate more relevant responses: Adding specific details or context to your queries helps the model understand the subtleties of your query and generate more relevant responses. For example, instead of a general question like “What’s the weather?” you can improve the promt by specifying the location and time interval: "What will the weather forecast be in New York tomorrow?"
Ask a model to assume a specific role or character: To enhance the dialogue experience, you can ask the model to assume a specific role or character. This will help create a more engaging and interactive conversation. For example, you can ask the model the following prompt: "You are a wise wizard. Give me your best advice for overcoming difficulties."
Use delimiters to clearly indicate individual parts of the input: Delimiters, such as special tokens or tags, can be used to indicate different sections of the input. This helps the model understand the structure and intent of your prompts. For example, if you want to provide a title and description, you can format the input as follows: "Title: 'Gardening Guide'\nDescription: 'Learn best practices for creating a beautiful garden.'"
Specify the steps required to complete a task: If you are instructing a model to perform a specific task, break down the instructions into clear steps. This will make it easier for the model to follow instructions and complete the task accurately. For example, if you want your model to bake a cake, provide step-by-step instructions: "Step 1: Preheat oven to 180°C. Step 2: Mix flour, sugar and eggs in a large bowl..."
Provide examples: Including examples in promts helps the model understand the expected output or behavior. The examples clearly demonstrate what you expect from the model. For example, if you want the model to generate poetry, you can include sample poems in the promt to show the style and structure you want.
Specify the desired output length: If you have specific requirements for the length of the generated response, indicate this in the prom. This helps the model produce output that matches your desired length. For example, you could state: "Please provide a brief summary in 2-3 sentences."

The “temperature” indicator is also available for the API, by adjusting it you can change the answer from the most accurate to the most creative (and possibly completely meaningless).

Overview of the capabilities of modern artificial intelligence platforms:

Natural Language Processing (NLP): Artificial intelligence (AI) is capable of understanding and processing human language at a more complex level than ever before. This allows it to generate text, translate languages, and answer questions in comprehensive and informative ways. Application examples include creating copy for marketing materials, developing chatbots for customer service, and writing personalized emails.

Computer Vision (CV): AI platforms are able to recognize and understand objects, people and scenes in images and videos. This allows you to classify images, detect objects in real time and generate realistic images and videos.

Machine Learning (ML): Systems can learn from data and improve their performance over time. This allows you to make predictions, optimize systems and personalize user experiences. Application examples include predicting customer behavior, optimizing supply chains, and personalizing user experiences.

Robotics: AI systems are capable of controlling and interacting with physical objects in the real world. This allows them to collect groceries, deliver packages, and even perform surgery.

These are just a few examples of the many ways artificial intelligence is being used today. With the development of artificial intelligence, we can expect even more amazing results from these technologies.