Is Corpus Curation same as Data Observability?

CohGent completes AI observability by exposing corpus-driven hallucinations

Corpus curation is not generally a part of data observability, but a complementary process within the broader field of data management. They are distinct concepts that work together to ensure overall goal of data quality and reliability. And CohGent expands the scope of curation by making the corpus less hallucinatory. Simply put, CohGent identifies & quantifies corpus-driven hallucinations and helps you fix at the source level.

Corpus Curation vs. Data Observability:

	Corpus Curation	Data Observability
Focus	Managing data (curation applied to text/language data)  over its entire lifecycle to ensure completeness, reliability and usability for specific purposes, such as AI model training or research.	Monitoring and understanding the health and behavior of data systems and pipelines in near real-time.
Purpose	To transform raw, error-ridden data into valuable, structured assets that are clean, organized, and ready for analysis or training AI/ML models.	To detect, troubleshoot, and resolve issues (anomalies, data loss, schema changes) in data pipelines quickly, using metrics, logs, and traces. #telemetry
Approach	Involves specific tasks like data cleaning, validation, enrichment, metadata management, and organization. This can use both automated tools and manual processes.	Relies on automated monitoring tools and real-time analysis to provide visibility into the data ecosystem, often leveraging AI and ML to detect changes and enrich metadata in real-time.

While distinct, the two concepts are intertwined and work together:

• Curation provides the foundation: Well-curated data provides the clean and reliable datasets necessary for effective data observability. You can’t observe the health of data if you don’t first define and prepare what “healthy” data is.
• Observability ensures ongoing health: Once a corpus is curated and put into a data pipeline, data observability tools monitor it to ensure it continues to behave as expected and meets defined quality metrics.
• Different focus: Curation is a hands-on management and preparation process, while observability is a monitoring and detection function. 
• Shared Goal: Both aim to ensure high data quality and reliability, which leads to better-informed decision-making, efficient operations and more effective AI initiatives. 

So, Data observability is about monitoring the system and data flow in real-time, while data curation is a more hands-on process of refining the actual content of the source data. In essence, you curate corpus to make it fit for purpose, and you use data observability to ensure it stays fit for purpose within a dynamic data ecosystem.

If you’re driving AIOps, MLOps, or invested in AI success, reach out to us today. With CohGent, you’ll solve nearly 30% of hallucination problems at the source, ensuring your AI projects deliver real results.