This study is based on developing an Artificial Intelligence program called KnowledgernBase System (KBS). It describes an investigation of the conventional Sexual TransmittedrnInfections (STI) syndromic approach problems, opportunities and the alternativernapproach to manage STI.rnThe conventional approach which is currently used for managing STL as evidencesrnindicated, has little impact on STI management or neither reduces most curable STI norrnHIV transmission. In addition to this, stigmatizing those who visit, unnoticedrnasymptomatic STI inflections and difficulty of notifying and managing sexual partner arernother drawbacks of the conventional approach. Thus, significant portions of thernpopulation, especially young people, still remain under information poverty about STI.rnAccording to FHI (2001), a comprehensive approach to manage STI is crucial tornalleviate such problems by promotion of partner treatment, prevention of reinjection,rnimprove health care seeking behavior, effective STI detection, and increasernawareness of Symptoms or risk One way considered here is designing andrndeveloping a prototype of KBS called Sexual Transmitted reflections Knowledge BasernSystem (SeTIKoBS). To build this system more than 16 medical staff, nine patients andrntwenty-jive historical cases were involved. Moreover to assess SeTIKoBS performancernfor the task for which it is designed, two evaluation techniques were used.rnFrom the experiment undertaken, SeTIKoBS evaluators judge the system effectiveness asrnan average rating of 87. 3 percent to manage STI which indicate a success and in somernsituations it is also noteworthy that the system is able to provide more accuraternmanagement than using conventional methods.