Best Practices for Data Quality in Generative AI

If all you're monitoring is your agent's outputs, you're fighting a losing battle. Beyond even embedding drift, output sensitivity issues, and the petabytes of structured data that can go bad in production, AI systems like agents bring unstructured data into the mix as well — and introduce all sorts of new risks in the process. When documents, web pages, or knowledge base content form the inputs of your system, poor data can quickly cause AI systems to hallucinate, miss key information, or generate inconsistent responses. And that means you need a comprehensive approach to monitoring to resolve it. Issue to consider: - Accuracy: Content is factually correct, and any extracted entities or references are validated. - Completeness: The data provides comprehensive coverage of the topics, entities, and scenarios the AI is expected to handle, where gaps in coverage can lead to “I don’t know” responses or hallucinations. - Consistency: File formats, metadata, and semantic meaning are uniform, reducing the chance of confusion downstream. - Timeliness: Content is fresh and appropriately timestamped to avoid outdated or misleading information. - Validity: Content follows expected structural and linguistic rules; corrupted or malformed data is excluded. - Uniqueness: Redundant or near-duplicate documents are removed to improve retrieval efficiency and avoid answer repetition. - Relevance: Content is directly applicable to the AI use case, filtering out noise that could confuse retrieval-augmented generation (RAG) models. While a lot of these dimensions mirror data quality for structured datasets, semantic consistency (ensuring concepts and terms are used uniformly) and content relevance are uniquely important for unstructured knowledge bases where clear schemas and business rules don't often exist. Of course, knowing when an output is wrong is only 10% of the challenge. The other 90% is knowing why and how it resolve it fast. 1. Detect 2. Triage. 3. Resolve. 4. Measure. Anything less and you aren't AI-ready. #AIreliability #agents

“Garbage in, garbage out” is the reason that a lot of AI-generated text reads like boring, SEO-spam marketing copy. 😴😴😴 If you’re training your organization's self-hosted AI model, it’s probably because you want better, more reliable output for specific tasks. (Or it’s because you want more confidentiality than the general use models offer. 🥸 But you’ll take advantage of the additional training capabilities, right?)  So don’t let your in-house model fall into the same problem! Cull the garbage data, only feed it the good stuff. Consider these three practices to ensure only high-quality data ends up in your organization’s LLM. 1️⃣ Establish Data Quality Standards: Define what “good” data looks like. Clear standards are a good defense against junk info. 2️⃣ Review Data Thoroughly: Your standard is meaningless if nobody uses it. Check that data meets your standards before using it for training. 3️⃣ Set a Cut-off Date: Your sales contracts from 3 years ago might not look anything like the ones you use today. If you’re training an LLM to generate proposals, don’t give them examples that don’t match your current practices! With better data, your LLM will provide more reliable results with less revision needed. #AI #machinelearning #fciso

Generative AI will only be as good as the unstructured data it’s built on. Contracts, emails, PDFs, SharePoint files—this is the proprietary content that gives AI context, makes it smarter about your business, and reduces hallucinations. But most organizations haven’t treated this data like a strategic asset. And now, gen AI is exposing the cracks. At MetLife, we are focused on improving unstructured data quality through both human and technical means. That includes context tagging, curation, governance, and building feedback loops into content creation. High-value AI doesn't come from deploying the latest tools. It comes from disciplined work on data quality, ownership, and purpose. Leaders who want AI to deliver business value need to treat unstructured data like infrastructure. Read more here: https://lnkd.in/eQ8QRsdg #GenerativeAI #DataStrategy #CDO #AILeadership #DataQuality