> ## Documentation Index
> Fetch the complete documentation index at: https://docs.guardion.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Feedbacks
> Improve your AI guardrails through our feedback loop system.
The feedback system allows you to continuously improve your AI guardrails by providing feedback on detection results. This human-in-the-loop approach helps fine-tune your detectors, reduce false positives, and enhance overall detection accuracy.
## How feedback works
When you provide feedback on detection results, Guardion uses this information to:
1. Build a dataset of edge cases specific to your use case
2. Immediately apply adjustments to incoming prompts and responses in the Guard API (using similarity and string match)
3. Incorporate your feedback during for the policy's models retraining
This process helps your guardrails become more accurate over time, reducing false positives while maintaining strong protection.
## Providing feedback
The feedback interface is integrated directly into the Logs section, making it easy to review and provide input as you investigate detections.
### Individual feedback
When reviewing a specific log entry:
1. Click on the log to view all detection details
2. For any incorrect classification, click **Mark as Misclassification**
3. Your feedback is immediately applied to the relevant policies
### Bulk feedback
To efficiently review multiple logs at once:
1. Select multiple log entries using the checkboxes
2. Choose one of the available actions:
* **Mark as Misclassification** - For incorrectly classified content
* **Confirm as Flagged** - To validate correct threat detections (available when Threat filter is enabled)
> **Note:** Feedback is processed individually for each policy that triggered a detection, allowing for precise improvement of specific guardrails.
## Reviewing feedback history
You can review all feedback provided for each policy:
The feedback history shows all previous inputs and allows you to modify them if needed. Any changes take effect immediately in the Guard API inside the feedback system.
## Feedback Integration with Guard API
The following diagram illustrates how the Guard API processes requests and incorporates feedback throughout the workflow:
```mermaid theme={null}
flowchart TB
Client[Client Application]
Client -->|POST /v1/guard| API[Guard API]
subgraph API[Guard API]
direction TB
Input[Target & Message Validation]
Policy[Policy Engine]
Detection[Detection Pipeline]
Eval[Threshold Evaluator]
Input --> Policy
Policy --> Detection
Detection --> Eval
end
subgraph Detection[Detection Pipeline]
direction LR
L1[**Layer 1:**
Feedback Database
Real-time updates]
L2[**Layer 2:**
Policy-Specific Models
Period updates]
L3[**Layer 3:**
Threat Intelligence
Foundation Models
Obfuscation Filters
]
L1 --> L2
L2 --> L3
L1 -.->|Flag| Result[Detection Result]
L2 -.->|Flag| Result
L3 -.->|Flag| Result
end
Eval -->|Response| Client
style L1 fill:#e3f2fd
style L2 fill:#e8f5e9
style L3 fill:#fff3e0
style Result fill:#ffebee
```
### Detection workflow
The Guard API follows a structured evaluation process for each request:
1. **Input validation**: The API validates incoming requests and extracts messages for evaluation
2. **Policy application**: Your configured policies determine:
* Which messages to evaluate (user, assistant, or both)
* Detection thresholds (L1-L4)
* Active detection layers
3. **Detection pipeline**: Each message passes through up to three detection layers
4. **Threshold evaluation**: Detection scores are compared against policy thresholds
5. **Response**: The API returns the evaluation result with detailed scoring breakdown
This layered approach ensures both performance and accuracy, with your feedback continuously improving detection quality.
### Detection layers
The Guard API uses a cascading detection pipeline that prioritizes speed and accuracy:
#### Layer 1: Feedback database
* **Method**: Exact string matching and semantic similarity
* **Speed**: \< 10ms
* High-confidence detection of previously flagged content
#### Layer 2: Policy-specific models
* **Method**: Models fine-tuned on your policy's feedback data
* **Speed**: \< 10ms
* Domain-specific threat detection
#### Layer 3: Threat Intelligence & Foundation models
* Comprehensive protection against zero-day threats
* **Method**: Guardion's state-of-the-art LLMs
* **Speed**: \~200ms
* Broad coverage and emerging threat detection
## Best practices
1. **Start with L2 threshold** and adjust based on your false positive tolerance
2. **Provide feedback regularly** to improve policy-specific model accuracy
3. **Monitor detection metrics** to optimize threshold configuration
4. **Use policy targeting** to evaluate only relevant message types
## Next steps
* [Configure your first policy →](./policies)
* [Submit feedback to improve detection →](./feedback)
* [View API reference →](./api-reference)