Trace the lineage of unstructured data: Understand data mapping and flows from source to end results, showing how the data moves from unstructured data systems to vector databases, to LLMs, and finally to endpoints.
Curate unstructured data: Automate the labeling or tagging of iraq whatsapp number data files to ensure that only relevant data with associated context is fed to GenAI models, thereby providing accurate responses with citations.
Sanitize unstructured data: Classify and redact or mask sensitive data from files that GenAI projects use.
Focus on the quality of unstructured data: Emphasize the freshness, uniqueness, and relevance of data to prevent unintended data usage in GenAI projects.
Secure unstructured prompts and responses with pre-configured policies: Detect, classify, and redact sensitive information on the fly, block toxic content, and enforce compliance with topic and tone guidelines.
Enterprises are eager to harness the power of generative AI, but many underestimate the complexity of managing unstructured data. Unlike structured data, unstructured information presents unique challenges that most organizations are ill-equipped to handle. By recognizing these distinct hurdles and implementing the best practices outlined above, companies can safely deploy genAI across their operations. This strategic approach not only mitigates risks but also positions organizations to fully capitalize on GenAI’s transformative capabilities, unlocking unprecedented value and innovation.