> ## Documentation Index
> Fetch the complete documentation index at: https://docs.remyx.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Insights
> Cross-experiment pattern detection and research-backed recommendations for what to try next
**Nav:** Experiments > Insights | **URL:** [`/experiments/insights`](https://engine.remyx.ai/experiments/insights)
A team runs 14 experiments in a quarter. Five explored retrieval and all worked. Three explored routing and none did. But nobody sees this pattern because each experiment is tracked in a different tool and nobody has time for meta-analysis across the full body of work.
Insights does that meta-analysis automatically. It groups your completed experiments by direction, computes which themes consistently produce positive results, and recommends what to try next based on your team's history.
***
## How Pattern Detection Works
Remyx analyzes all completed experiments in the current project through three steps:
Experiments are grouped by their tags. Each tag with 3+ completed experiments becomes a cluster to evaluate.
For each cluster, Remyx computes what fraction of experiments produced a positive, statistically significant delta.
Each cluster is classified based on average delta and hit rate:
| Signal | Criteria | Meaning |
| ------------------ | ----------------------------------- | -------------------------------------- |
| **High** (green) | Avg delta ≥ 1.5% and ≥ 50% positive | This direction consistently works |
| **Low** (red) | Avg delta ≤ 0.5% or ≤ 50% positive | This direction isn't producing results |
| **Mixed** (yellow) | Between high and low | Inconclusive — needs more experiments |
***
## The Insights View
The page is organized into three sections by signal strength:
### Strong Directions
Clusters where the team's experiments have consistently produced positive results. These are the directions worth doubling down on.
### Mixed Signal
Clusters with inconclusive results — some experiments positive, some not. These may need more experiments or a refined approach.
### Weak Directions
Clusters where experiments haven't produced meaningful results. Useful signal — knowing what *doesn't* work prevents wasted effort.
***
## Cluster Details
Each cluster appears as a collapsible row showing:
| Element | Description |
| -------------------- | ---------------------------------------------------------- |
| **Tag name** | The grouping tag (e.g., "retrieval", "prompt-engineering") |
| **Signal badge** | HIGH / LOW / MIXED with color |
| **Positive count** | "5 of 5 significant" |
| **Avg delta** | Average observed improvement across the cluster |
| **Experiment count** | Total experiments in this cluster |
Expand a cluster to see two columns:
**Left: Experiment list**
* Each experiment with its name, delta, status, and decision summary
* Click any experiment to navigate to its [detail view](/platform/experiments/outcomes#experiment-detail)
**Right: Recommended resources**
* Research-backed next steps: papers, repos, or models whose methods align with this cluster's direction but haven't been tried yet
* Each recommendation shows title and a link to the resource viewer
***
## Starting from a Recommendation
Each recommended resource has a **Start Experiment** button. Clicking it creates a new experiment with:
* The resource linked as the source
* The cluster's tag pre-filled
* The current project selected
This closes the loop: your experiment history informs what Remyx recommends next, and the recommendation becomes your next experiment.
***
## Context Line
At the top of the page, a summary line shows the scope:
> *"5 directions across 14 experiments"*
This differentiates the Insights view from the [Projects](/platform/manage/projects) (which shows project-level health) — Insights operates at the tag/direction level within a single project.
***
## When Insights Appear
Insights require a minimum of **4 experiments with shared tags** to surface meaningful patterns. With fewer experiments, the page shows an empty state encouraging more experimentation.
**Tags drive pattern detection.** Consistent, descriptive tagging across experiments is what makes Insights useful. Use reusable tags that describe the *direction* (e.g., `retrieval`, `prompt-engineering`, `tool-use`) rather than one-off labels.
***
## Example
After 14 experiments on a Customer Support AI project:
```
retrieval (5 experiments): 5/5 positive, avg +3.2% → HIGH SIGNAL
tool-use (1 experiment): 1/1 positive, avg +4.2% → HIGH SIGNAL
prompt-engineering (2 experiments): 2/2 positive, avg +1.2% → MIXED
routing (2 experiments): 0/2 significant, avg -0.2% → LOW SIGNAL
```
Remyx recommends two next experiments in the retrieval direction: multi-hop retrieval and retrieval-augmented tool selection — combining the two strongest clusters.
***
## Related
View and manage individual experiments
Portfolio view across all projects
The methodology behind pattern detection