FAQ
Honest answers to the questions that come up most — especially the "why did it do that?" kind about the Knowledge layer. Graphium's design goal here is that every number you run into is either a cost you decided or a value measured from your corpus, never a hidden quality filter. Click a question to expand its answer.
Why am I getting few Insights?
Insights are abstractions: the model reads your Claims and tries to factor out a rule that would survive outside its original context (the "portability test"). When few appear, check these in order:
- Do your Claims converge? A collection of one-off facts has no recurring pattern to abstract. The model is explicitly told that an empty list beats a forced Insight, so zero can be the honest answer for a young or scattered corpus. Insights start appearing when several Claims circle the same rule.
- Which model is doing the abstracting? Insight generation uses the Chat & Insight model from Settings. Abstraction is the hardest step in the pipeline, and smaller local models often return empty lists or mere restatements where a stronger model finds real patterns. We recommend a frontier-tier model here (for example Claude Opus or Claude Sonnet) — being "reasoning-capable" or merely large is not always enough; see the guide table in Setting up AI. If Insights are rare, try assigning a stronger model and running discovery again — same corpus, one variable changed. If a strong model still finds nothing new, the material (not the software) is the limit.
- Are new candidates reinforcing instead of creating? A candidate that closely matches an existing Insight is not dropped — it is folded into that Insight as additional support. The result panel counts these as reinforced. If you see reinforcements, discovery is working; your existing Insights are getting stronger rather than multiplying.
- Has everything actually been looked at? Scans report their measured coverage as covered n/m — how many of your Claims were in view at least once. Ingest-time scans are budgeted (see the next question), so a large corpus is only partially covered per ingest by design. To sweep everything, run Discover Insights from Claims in Settings → Knowledge: it shows the measured number of LLM calls needed for full coverage before running, and reports the coverage it reached.
Does Graphium secretly limit how many Insights or Claims are created?
No. There are no quality-based silent drops: the model's confidence score is recorded and shown, never used to discard a candidate, and there is no minimum or maximum count a scan must produce. Claims work the same way — the extractor is told to harvest every distinct transferable point, with no fixed cap.
The numbers that do exist fall into three categories, and each has a reason you can check:
| Number | What it is | Why it exists |
|---|---|---|
| Scans per ingest (default 3) | A user setting in Settings → AI. Each scan is one LLM call over one cluster of Claims | Cost control — yours to decide. Set 0 to skip scanning at ingest entirely; the result always reports the measured coverage so you know what was and wasn't looked at |
| Calls needed for full coverage | Shown in the confirm dialog of Discover Insights from Claims | Measured, not configured. Computed from how your Claims cluster; scanning runs until every Claim has been in view at least once |
| ~50 Claims per LLM call, previews truncated | Internal slice size | Context-window protection — more would silently overflow the model's input |
| Duplicate threshold (embedding similarity) | Splits candidates into "new" vs. "matches an existing Insight" | Near-duplicates become reinforcement, not new pages. Nothing is discarded — the matched Insight gains the new supporting Claims. Without an embedding model this check simply passes everything through as new |
| 0–8 candidates per call, up to 3 relations per Insight | Prompt-side ranges | Anti-runaway guards on LLM output verbosity, not caps on your knowledge — repeated scans keep adding what they find |
| At least 2 Claims before scanning | Run precondition | Cross-Claim patterns need something to cross; with a single Claim there is nothing to scan against yet |
| Unknown source IDs dropped | Parser guard | Hallucination defense — a candidate citing a Claim that doesn't exist is discarded rather than saved with a broken reference |
If you ever see behavior that looks like a hidden filter and isn't explained by this table, that's a bug worth reporting.
How do I tell whether it's Graphium or my model?
Run one controlled experiment: open Settings → Knowledge → Discover Insights from Claims, and note the result line — created, reinforced, and covered n/m. Full coverage with reinforcements but no new Insights means the pipeline looked at everything and your existing abstractions already cover the patterns present. Then assign a stronger Chat & Insight model and run it once more. If the stronger model creates Insights the smaller one didn't, the model was the bottleneck; if not, your Claims genuinely don't hold new recurring patterns yet — write more notes and let them converge.
What's the difference between Claims and Insights?
A Claim is one grounded assertion extracted from your notes; an Insight is a pattern that recurs across Claims. They form the narrow waist of the hourglass: notes → claims → insights. See the Knowledge layer page for the full picture.