Identifying a clear and specific problem is the foundation of building an effective AI chatbot.

Key actions:

• Determine your users' pain points or goals.
• Decide whether the chatbot will answer FAQs, generate content, provide customer support, etc.
• Write down common user scenarios or tasks you want Adhmiyam AI to handle.

Example:

• We want the chatbot to help users generate marketing copy based on input prompts.

Choose the right NLP model (like transformer-based models) and train your chatbot using relevant datasets to fulfill your use case.

Choose a Platform

• Around 2013, the rise of deep learning — especially transformer-based architectures like BERT and GPT — revolutionized natural language processing (NLP).
• Today, platforms like OpenAI, Hugging Face, and Google Cloud offer APIs and models that Adhmiyam AI can build upon for scalability and performance.

Research market competitors, define your feature set, and begin development using the framework that fits your product vision.

Deploy your MVP and gather feedback. Initial users help validate your product direction and uncover usability issues.

Regularly improve your app based on user input, bug reports, and evolving AI capabilities.

Gather a wide range of user inputs and annotate them to build a robust training dataset.

Scale intelligently. Add multimedia input support, voice commands, or integrations to increase utility.