Advanced Portal Use
Tune extraction windows, source filters, controlled reruns, and evaluation experiments.
Advanced Portal Use
Hosted Enterprise
Start with Reflexio's defaults. Change one control at a time only after the portal shows a concrete quality, latency, or cost problem.
Tune Extraction Cadence
window_size controls how much interaction context one extraction pass sees. stride_size controls how many new interactions advance the next window.
| Goal | Adjustment | Tradeoff |
|---|---|---|
| Faster learning | Smaller window or stride | Less context and more frequent model calls |
| Richer synthesis | Larger window | More latency and token use per pass |
| Lower extraction cost | Larger stride | New evidence waits longer for the next pass |
Keep stride_size <= window_size. Evaluate changes with representative sessions rather than one short conversation.
Restrict Learning by Source
The profile and playbook extractor each support an optional list of enabled request sources. Leave it empty to learn from all sources; specify stable source values when a workflow should be included or excluded deliberately.
Use source filters for product boundaries such as support_chat or coding_assistant, not for individual experiments or ad hoc labels.
Each organization has one profile extractor and one playbook extractor. Source filters narrow those extractors; they do not create multiple named extractors.
Use Manual Mode Deliberately
Manual profile mode is useful when a workflow has a clear completion boundary and profiles should be created only after that boundary.
- Set the profile extractor to Manual in Extractor Settings.
- Continue publishing complete interactions normally.
- From the Current Profiles view, run profile generation with the required user, source, or time filters.
- Inspect the result before using the same pattern in production.
The client equivalent is manual_profile_generation(user_id=..., source=...). The legacy extractor_names parameter is accepted for compatibility but does not select an extractor.
Rerun After Configuration Changes
Use rerun operations when you want to compare a new extraction definition against existing evidence. Reruns create pending results so you can inspect them before replacing the current set.
- Narrow by source and time to control scope and cost.
- Change one prompt or threshold at a time.
- Review pending results before adoption.
- Record the configuration and agent version used for the experiment.
Do not wire rerun or manual-generation endpoints into the normal publish path. Automatic extraction is the production default.
Search and Diagnose
When a user reports irrelevant context:
- Confirm the source interaction is complete.
- Inspect the current profile or user playbook and its source request.
- Reproduce retrieval with the same user, agent version, query, and tags.
- Adjust retrieval thresholds only after confirming extraction itself is correct.
Use Searching Learned Context for exact search behavior and filters.
Measure Changes
Use source-set comparisons for cohort-level experiments and shadow responses for per-turn regular-versus-shadow comparisons. Keep source labels and agent versions stable so results remain interpretable.
See Measuring Reflexio's Impact before enabling an experiment; it defines the traffic, sampling, and comparison contract.