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Quick Start: Building Your First Chart

Creating insightful visualizations in HoneyHive is straightforward. Follow these steps to start monitoring your LLM application performance:

  1. Access the Chart Builder

    • Click “New Chart” in your Dashboard, or
    • Navigate to the “Discover” tab from the sidebar

  2. Select Your Data Source

    • Choose from three data scopes:
      • Sessions: Full user interactions/traces (entire conversations)
      • Completions: Individual LLM calls
      • All Events: Any tracked step in your pipeline, including tool calls

  3. Configure Your Visualization

    • Event: Select which specific event type to analyze (default: All Sessions/Completions/Events)
    • Metric: Choose what to measure (e.g., Request Volume, Duration, Cost, or custom evaluators)
    • Aggregation: Decide how to calculate (Sum, Average, Median, 99th Percentile, etc.)

  4. Refine Your Analysis (Optional)

    • Filter: Narrow down to specific data segments (e.g., source = "production")
    • Group By: Split results by properties (e.g., prompt_version, model, `user_tier)
    • Time Range: Set your analysis window (1d, 7d, 30d, etc.)

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Understanding Your Data

To build effective charts, it’s crucial to understand the data components available in HoneyHive:

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Metrics

Metrics are the numerical values you’ll visualize in charts:

  1. Usage Metrics

    • Request Volume: Queries over time. Spot usage spikes or drops.
    • Cost: Direct expenses. See if that new feature is breaking the bank.
    • Duration: System latency. Because slow responses kill engagement.

  2. Evaluators

    • Definition: Your custom quality checks, either Python or LLM-based.
    • Requirements: Must return float or boolean to chart.
    • Examples:
      • Keyword Presence (boolean): “Does every product review mention the product?”
      • Coherence Score (float): “How logically sound are multi-turn conversations?”

  3. User Feedback

    • Definition: The voice of your users, quantified.
    • Requirements: float or boolean inputs.
    • Examples:
      • Usefulness Rating (float): “On a scale of 1-5, how useful was this response?”
      • Used in Report (boolean): “Did the user actually use this in their report?”

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Properties

Properties provide context for your metrics. All properties in the data model such as config, user properties, feedback, metrics, and metadata can be used to slice and dice your data.

Metrics chart performance. Properties unveil the context behind that performance. Both are crucial for exploratory data analysis.

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Chart Types in Detail

Each chart type in HoneyHive focuses on different parts of your LLM pipeline:

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Completion Charts

  • Focus: Individual LLM calls.
  • Key Metrics: cost, duration, tokens, errors, and any specified evaluators.
  • Example Use Case:
    • Hypothesis: “Longer user messages cause more token waste.”
    • Test: Chart Average Unused Output Tokens grouped by binned_input_length.

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Session Charts

  • Focus: Full user interactions and entire traces.
  • Key Metrics: User Turns, Session Duration, Avg User Rating, Agent Trajectory.
  • Example Use Case:
    • Hypothesis: “Agents start looping after n turns.”
    • Test: Chart Agent Trajectory Evaluator grouped by Number of turns.

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Event Charts

  • Focus: Specific steps or tools.
  • Key Metrics: Retrieval Latency, Synthesis Quality, Tool Choice Accuracy.
  • Example Use Case:
    • Hypothesis: “Our reranker is the bottleneck in high-load scenarios.”
    • Test: Chart 99th Percentile Rerank Time vs. Requests per Minute.

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Advanced Chart Building Techniques

1

Choose Your Metric (What to Measure)

  • Process: Pick chart type, then a relevant metric.
  • Real-world Usage:
    • Don’t just track Request Volume. Ask: “Is volume growing faster for paid or freemium?”
    • Beyond Cost, ponder: “Is cost per successful session decreasing over time?”
2

Apply Aggregation (How to Measure)

  • Key Functions:
    • Average: Typical case. “What’s our usual response time?”
    • 99th Percentile: Edge cases. “How bad does it get for our unluckiest users?”
    • Percentage True: For booleans. “What % of responses are factually correct?”
  • Real-world Usage:
    • Average is good, but Median might better represent a skewed distribution.
    • Watch both Average and 99th Percentile to catch issues averages hide.
3

Filter and Group (Segmenting Data)

  • Filtering:
    • Syntax: property operator value. E.g., industry == "finance".
    • Examples:
      • topic_category != "smalltalk" to focus on core use cases.
      • embedding_model == "v2" AND date > model_switch_date for before/after analysis.
  • Grouping:
    • Syntax: Select properties. E.g., prompt_template, user_tier.
    • Examples:
      • prompt_template to see which prompts waste tokens.
      • user_tier and topic_category to see if premium users ask harder questions.

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