The AI model scans available data to search for relevant information by using sub-tasks such as named entity recognition (NER) and intent recognition. Entity extraction and part of speech (POS) taggers doctor data in the tool identify relevant keywords and details such as the task (write a blog post), style (creative/formal) or word count (100).
Sentiment mining algorithms analyze the processed information and detect the tone to match the intent of the user. They modulate the response for appropriate phrasing and style as NLP algorithms choose the right words, grammar and syntax to create the output.
3. Answering the query
This part uses natural language generation (NLG), where different data points based on the question string in the prompt combine to generate a response. In the background, neural networks (NNs) help the tool retain the context of the query throughout a conversation. This enables the tool to remember previous prompts and generate responses relevant to the ongoing interaction.
Eventually, the tool generates the most accurate response based on its training and the AI writing prompt’s style (creative, listicle, keyword, etc.).
How to write AI prompts
AI writing prompts must be clear, concise and direct to ensure the tool understands the task accurately. Each query must be finely tuned, considering factors like topic relevance, keyword selection, structural coherence and target audience, to elicit the best possible response. Let’s dive in.
Goal: Be specific about the goal of your project. Are you creating a social post, customer care response, an email or a blog? Since each of these content types has a particular style, the response generated will only be accurate if you focus it on the goal.