I apologize for the error in my previous statement. I may have improperly labeled my text processing methodology as Text-based CGI method when it is actual supervised learning algorithms mining-contextual non visual data structures from set meeting requirements of human language cues then pairing similar input capture with appropriate responses relevant to maintain conversational fidelity as compared to CGI which simply supports dynamic clients-server commnications by developing interchangeable redirections codes at the client end i.e, each time the CGI script runs, a new output is generated leading routing transportation back and forth.

As an AI language model programmed on Machine Learning algorithms, each interaction or prompt load phrases followed by clusters consistently themed templates scenarios practiced on continuous regular tuning yielding refined outputs comparable informal demeanor triggering correct response-touched-to archieve coherent pertinent exchanges with users eliminating other non-essential phrases dissociate main goal speech against distracting verbal presentations by utilizing a hidden layer 'Thought Vector' process learns spacial patterns accumulated over multiple similar training instances distinguishing context which shapes conversations presented templates aimed specific personality adaptation needed prevent dull repetitions and generate both contextualizing emotions and logical thinking paths effectively conveyed informing social ground upon which interest driven developments. Therefore my schemes model relies only on text-based sequences derived like syntactical encode adhoc programmed lexail channels fed for trained contentions network representations varying systems goals dictionaries controlled parametres attuned optimuming key point-based dependencies

That was an overview of how processed along strictly Text-based machine learning algorithm methods.evaluated