> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# User Dashboard

> The User Dashboard provides a consolidated view of all interactions, sessions, and traces linked to a specific user. It enables LLM application developers to debug issues, analyze behavior patterns, and optimize resource allocation at the individual user level.

## Key Features

* **Unified User Journey View**: Consolidates all traces, sessions, and metrics related to a specific user into one tab, eliminating the need to manually piece together their journey.

* **Efficient Debugging**: Quickly isolate and investigate a user's reported issue by viewing all associated sessions and anomalies.

* **User-Level Quality Metrics**: Track satisfaction scores, frustration indices, and success rates at the individual level.

* **Behavioral Insights**: Identify patterns such as engagement frequency, query evolution, task completion rates, and guardrail triggers.

* **Resource Optimization**: Detect power users, problematic users, or high-cost accounts to inform allocation strategies.

* **Search & Filtering**: Search by UserID and apply filters across date, metrics, and custom attributes.

## How to Use the User Dashboard

### 1. Pass User Identifiers in Traces

When creating a trace or span, include `user.id` and optional metadata to associate interactions with a specific user:

```json theme={null}
with using_attributes(
    session_id="new-session",
    user_id="newuser",
):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Write a haiku."}],
        max_tokens=20,
    )
```

OR

```json theme={null}
from fi_instrumentation import register, FITracer

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="PROJECT_NAME"
)

tracer = FITracer(trace_provider.get_tracer(__name__))

with tracer.start_as_current_span(
    f"SPAN_NAME",
) as span:
    span.set_status(Status(StatusCode.OK))
    span.set_attribute("user.id", "vivek.gupta")
    span.set_attribute("user.id.type", "email | phone | uuid | custom")
    span.set_attribute("user.id.hash", "<hash_for_the_user.id>")
    span.set_attribute("user.metadata", {})
    span.set_attribute("fi.span.kind", "llm")
    span.set_attribute("llm.provider", "claude")
    span.set_attribute("input.value", "input")
    span.set_attribute("output.value", "output")
```

### 2. Explore the Dashboard

The Dashboard displays a paginated table with:

* **UserID**
* **Activation Date**
* **Last Active Date**
* **Count of Traces / Count of Error Traces**
* **Count of Sessions**
* **Average Latency (Trace & Session)**
* **Total LLM Calls**
* **Evaluation Pass Rate**
* **Guardrail Trigger Count**
* **Total Tokens (Input, Output, Total)**
* **Total Cost**

### 3. Drill into User Details

Click on any **user.id** to open a detailed view containing:

* **Summary**: Total traces, cost, active days, average latency, total sessions, session duration, task completion rate, satisfaction score, and % successful sessions.
* **Traces Tab**: Trace ID, session ID, latency, input/output, evaluation results, cost, annotations, and full trace details.
* **Sessions Tab**: Session ID, start/end time, # of traces, session-level evals, cost/tokens, first/last message, status, and filters by date, status, duration, or cost.
* **Behavioral Insights**: Engagement trends, anomalies (e.g., spikes in errors), and guardrail triggers.

### 4. Apply Filters & Search

Filter by:

* Date range
* Trace ID
* Evaluation metrics
* System metrics
* Custom attributes

Search across the **User Tab**, **Sessions**, or **Traces** using UserID.

By leveraging the User-Level Tab, teams can proactively manage user experiences, accelerate debugging, and gain deep behavioral insights to improve product quality and personalization.