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Real-World Use Case – Making Your Application Available on ChatGPT
In earlier chapters, we learned quite a lot. We learned how to create and optimize prompts, how to create semantic and native functions and put them in Semantic Kernel, and how to use a planner to automatically decide which functions of the kernel to use to solve a user problem.
In the previous two chapters, we learned how to augment our kernel with memories, including memories built from external data, which allows us to build more personalized applications and use data that is recent and that we have control over to generate answers, instead of using only the data that was used to train the LLM, which is frequently not public.
In this final chapter, we will change gears. ...
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