Building an application like ChatGPT involves several key steps:

### Understanding and Preparation:
1. NLP Fundamentals: Learn natural language processing (NLP) and deep learning principles.
2. Data Collection: Gather a large text dataset (books, articles, conversations) and preprocess it by cleaning and tokenizing.

### Model Development:
3. Choosing a Model: Use an existing model like GPT-3 or develop your own using architectures like the Transformer.
4. Training : Train the model on your dataset, requiring substantial computational resources and hyperparameter tuning.
5. Fine-tuning: Adjust the model on specific datasets to enhance performance for conversational tasks.

### Infrastructure and Deployment:
6. Hardware and Cloud Services: Use powerful GPUs/TPUs and cloud platforms (AWS, Google Cloud, Azure) for scalability.
7. Model Hosting: Deploy the model on a server to enable API access.

### Application Development:
8. Backend: Develop an API to handle model requests.
9. Frontend: Build a user interface (web, mobile, or desktop) for interaction.
10. Integration: Connect the frontend to the backend API.

### Testing and Improvement:
11. Performance Testing: Evaluate the model using metrics and user feedback.
12. Continuous Improvement: Monitor, update, and refine the model and application.

### Ethical Considerations:
13. Bias and Privacy: Address model biases and ensure data privacy and security.

Using libraries like TensorFlow, PyTorch, and Hugging Face’s Transformers, and leveraging cloud services can streamline the process.