Chatbot system and method mimicking an expert while responding to user queries using integrated programmatic and specialized guided and constrained artificial intelligence

A system of guiding and constraining an AI-based chatbot to provide responses emulating an expert, the method comprises:

• one or more processors of a computer system;
• a memory, coupled to the one or more processors, that store code and execution of the code by the one or more processors causes the computer system to perform operations comprising:
• providing access to a knowledge database including training data, wherein the training data includes a plurality of knowledge documents by the expert;
• creating a vector database using the training data, via chunking and embedding techniques, wherein the training data is segregated into a plurality of chunks and each chunk refers to a unique topic;
• receiving a user query, via a chatbot interface, related to a topic;
• running a semantic similarity search in the vector database, based on the received user query, to identify one or more chunks relevant to the topic;
• processing the identified chunks to generate a plurality of thoughts, wherein thoughts are referred to by an AI engine for generating a response relevant to the user's query while emulating the expert;
• generating prompts to guide the AI engine to generate the relevant response emulating the individual, wherein the prompts include guidelines for using the thoughts and boundary conditions considered by the AI engine while responding to the user's query;
• generating the response relevant to the user's query, wherein the generated response is coherent with the expert's knowledge and emulates the expert providing access to a data model comprising educational standards and stimulus types, wherein the educational standards are mapped to one or more relevant stimulus types;
• providing access to a repository of JSON schemas and Python functions, wherein each stimulus type is mapped to at least one JSON schema and one or more Python functions, thereby mapping the educational standards to relevant JSON schemas and Python functions;
• selecting a relevant JSON schema and one or more Python functions based on the stimulus type of the received input query;
• generating prompts to guide and constrain the AI engine to populate the selected JSON schema based on inputs received from the data model and input query, wherein the prompts include one or more functions for generating stimulus descriptions relevant to the mapped stimulus type;
• transferring the prompts to the AI engine for populating the JSON schema;
• calling one or more Python functions, via a Python function module, to generate a stimulus image, wherein the Python function module accesses one or more libraries for rendering the stimulus image;
• and
• storing the generated stimulus image in a stimulus database, wherein storing the stimulus image includes tagging the stimulus image to an associated mathematical question.