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The best way to overcome this hurdle is to go back to data basics. Organisations need to build a strong data governance strategy from the ground up, with rigorous controls that enforce dataquality and integrity. The many obstacles holding companies back from rolling out AI tools can be overcome without too much trouble.
This is doubly true for complex AI systems, such as large language models, that process extensive datasets for tasks like language processing, image recognition, and predictive analysis. Only then can we raise the potential of AI and large language model projects to breathtaking new heights.
The following sections further explain the main components of the solution: ETL pipelines to transform the log data, agentic RAG implementation, and the chat application. Creating ETL pipelines to transform log data Preparing your data to provide quality results is the first step in an AI project.
Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Techniques such as data cleansing, aggregation, and trend analysis play a critical role in ensuring dataquality and relevance.
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