Organizing data with RAG for accurate and reliable LLMs
AI adoption is accelerating, but in regulated industries, the model itself isn’t the roadblock, it's the data.
Most organizations still rely on messy, unstructured documents, PDFs, CAD drawings, handwritten notes, that models can't interpret with accuracy or compliance confidence.
KMWorld recently held a webinar, “Improve LLM Accuracy with RAG,” featuring Jason Jakob, chief architect officer at Adlib, who discussed how Retrieval-Augmented Generation (RAG) can be made reliable at scale.
Unstructured data is the number one blocker for AI, Jakob explained. He cited various studies reporting that 80% of the time spent on AI projects is devoted to data preparation tasks and 60% of AI projects lacking AI-ready data will be abandoned by 2026.
RAG reduces hallucinations by grounding answers in documents. But its accuracy depends entirely on the quality of the source documents and how the content is chunked for search. Poorly structured or context-less chunks still lead to misleading or missing answers.
Weak inputs, not weak models, are the number one reason AI initiatives fail. Garbage in equals garbage out, Jakob said.
Adlib improves RAG outcomes by delivering higher accuracy, reduced hallucinations, and compliant, industry-grade responses. The platform provides true accuracy outcomes with preprocessed AI-ready governed content into models automatically.
Adlib extends the life of existing DMS/ECM and adds missing AI RAG Search regardless of what DMS\ECM is the source data is coming from. Adlib integrates OCR and LLM to enhance AI RAG search results.
According to Jakob, Adlib’s complete pipeline includes:
- Custom Workflows
- Pre-Processing and Repair Intelligent
- OCR and LLM
- Document Classification
- Industry Taxonomies
- Extract Entities and Key Data
- Critical Validation w Lookups
- HITL Review Exceptions
- Generate Metadata
- Hybrid AI RAG Storage
- Audit Records/Security/Compliance
- AI Chat with Docs
- Downstream Data
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.