Iteration and improvement
Start focused. Learn from the answers. Improve deliberately.
A practical way to evolve organizational AI: test a useful question, find the missing context and make a targeted change.
Change the application. Keep the knowledge.
Improve the Expert by improving its knowledge.
A first version does not need your entire document estate. It needs enough relevant information to answer a useful set of questions. Then the answers tell you what to change.
Use a short learning cycle.
1. Establish a baseline
Start with your primary website. Ask representative questions and compare the answers with information you trust.
2. Find the missing context
Identify whether a weak answer reflects missing source material, ambiguous wording or a question outside the Expert’s purpose.
3. Add the smallest useful source
Upload the document that addresses that gap, or improve the existing source. Repeat the same questions to judge the difference.
Give content changes a predictable path into the Expert.
Static uploads can be replaced manually. On Business, connected sources are checked weekly; on Enterprise, daily. Delta processing skips unchanged files and reprocesses only new or changed content.
The schedule describes when sources are checked. Source availability, document volume and processing time affect when an update becomes usable.
Treat updates as knowledge changes, not a perfect undo function.
An update to the same document does not count as a new document. It may replace or extend the graph, but it cannot guarantee selective removal of every historical fact. Deleting the source also leaves learned knowledge intact.
When a complete reset is required, the Account Owner can delete the Knowledge Base. Build important source changes into your answer-review routine instead of assuming that ingestion alone proves correctness.
Separate content improvements from model decisions.
If the Expert lacks a product detail, changing the reasoning model does not supply your unpublished specification. Fix the knowledge gap first. If the knowledge is present but interpretation is weak, review the Expert configuration and the supported reasoning option.
This separation is the practical benefit of keeping knowledge independent of one model: content, configuration and processing choices can be assessed on their own merits.
Expand when the evidence supports it.
A useful next step may be more document capacity, a connected source or a different processing environment. It should follow a demonstrated need in the questions and content—not the assumption that every pilot needs a large AI implementation.
Make the next version better at one important job.
Keep a repeatable question set. Change the relevant source or configuration. Compare the answers. That gives you a practical basis for deciding what to expand next.