To build an application like ChatGPT, you would typically follow these steps:

1. Define Objectives and Use Cases: Clearly outline the purpose of your application. Determine if it will provide general conversation, domain-specific knowledge, customer support, or something else.
2. Select Technology Stack: Choose appropriate technologies based on your objectives. Common choices include Python for backend development, frameworks like Flask or Django for web applications, and machine learning frameworks such as TensorFlow or PyTorch for natural language processing (NLP).
3. Data Collection and Preparation: Gather and prepare datasets relevant to your application’s domain. For NLP tasks, this could include text data for training your model on various topics and conversation patterns.
4. Model Training and Development: Train a language model using deep learning techniques. Pre-trained models like GPT (Generative Pre-trained Transformer) can be fine-tuned on your specific dataset to better understand and generate human-like responses.
5. Integration and Deployment: Integrate your trained model with your chosen backend technology. Deploy the application on cloud services like AWS, Azure, or Google Cloud to handle user requests and responses efficiently.
6. Testing and Iteration: Test your application rigorously to ensure it handles various inputs and scenarios effectively. Gather feedback and iterate on your model and application based on user interactions and performance metrics.
7. Maintenance and Scaling: Continuously monitor and update your application to improve performance, handle increased traffic, and incorporate new features or improvements in NLP technology.

Building an application like ChatGPT requires a blend of expertise in NLP, machine learning, software development, and cloud computing. It’s essential to tailor each step to fit your specific use cases and technical requirements for a successful and scalable application.