Security in Enterprise AI
Understand how to feed your AI with structured data, documents, and live content—accurately and efficiently.
Feeding your AI the right data is essential to generating relevant, high-quality responses. In this tutorial, you’ll learn the differences between direct file uploads, searchable knowledge bases, and dynamic vector stores in StackAI. Each option is designed for different document sizes, use cases, and performance needs—so you can build workflows that are both scalable and reliable.
Summary
Files Node: upload full PDFs, Word docs, or text files for direct processing
Adjust chunk size and overlap to optimize model input and control citations
Enable text/image extraction and advanced parsing for charts and tables
Use public file uploads (user input) or private file nodes (internal reference)
Use URL scraping, audio, or image nodes for non-textual inputs
Difference between Files Node vs. Knowledge Base:
Files: send entire doc to model
Knowledge Base: send only relevant excerpts via search
Configure knowledge base indexing: top results, chunk size, page matching, etc.
Expose knowledge base search to end users as part of your app interface
Use dynamic vector stores for very large docs that can’t be indexed ahead of time
Choose data strategy based on:
Document size
Context limitations
Risk of hallucination
Precision of query needs
Next up: put it all together to build accurate, context-aware AI workflows