This page covers AI model selection for Lyra coaches and the analytics data available for monitoring coach usage.
On this page: AI model selection | Analytics
AI model selection
Lyra coaches are powered by large language models (LLMs) — the AI technology that enables conversational, context-aware responses. When configuring a coach, you can select which AI model it uses. Different models have different strengths, and your choice will affect the coach's response quality, speed, and behaviour.
Available models
|
Model |
|---|
|
GPT 4.1 |
|
GPT 4o |
|
GPT 5 |
|
GPT 5 Thinking |
|
GPT 5 Mini |
|
Claude Sonnet 4 |
|
Gemini 2.5 Pro |
|
GPT 5.1 |
|
GPT 5.1 Thinking |
|
Claude Sonnet 4.5 |
|
GPT 5.2 |
|
GPT 5.2 Thinking |
|
Claude Sonnet 4.6 |
|
Claude Opus 4.6 |
How to choose a model
For most coaches, the default model will work well. Consider changing the model if you have specific requirements around response style, speed, or if you require complex thinking.
For general-purpose coaching and knowledge retrieval, a flagship model like GPT-4o or Claude Sonnet will give the most capable, well-rounded responses. These are the best choice for coaches that need to handle a wide range of questions, reason through complex scenarios, or follow nuanced instructions in the Purpose field.
Things to keep in mind:
Model updates happen periodically. AI model providers regularly release improved versions. When a model is updated, your coaches will benefit from improvements in reasoning, accuracy, and speed without requiring any changes to your configuration.
Changing models doesn't change knowledge. The AI model affects how the coach reasons and responds. The information it draws on is determined by your knowledge base. Switching models won't add or remove knowledge; it changes how the coach processes and communicates that knowledge.
Analytics
Lyra captures usage data for each coach, which can be shared with admins on request. This data is currently provided manually and is not yet connected into Fuse Universal Analytics, integration with our platform's native reporting is on the roadmap.
Available data points
|
Metric |
Description |
|---|---|
|
Active users |
Unique users who have engaged with any Lyra coach in a given period |
|
Total sessions |
Number of coaching sessions started across all coaches |
|
Total threads |
Number of individual conversation threads opened |
|
Sessions by coach |
Which coaches are getting the most use |
|
Top themes |
The most common topics users are asking about across coaches |
|
Messages sent |
Total messages sent to a specific coach |
|
Number of threads |
Total conversation threads opened with that coach |
|
Average messages per thread |
Mean conversation depth — how many exchanges a typical conversation involves |
What to look for
-
Active users over total messages. A coach with 50 active users having meaningful conversations is more valuable than one with 5 power users sending hundreds of messages.
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Low messages per thread. If users are opening threads but only sending one or two messages, they may not be finding value. Review the knowledge base and purpose — the coach may not be answering what users actually need.
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Top themes vs. knowledge base coverage. If a theme is popular but the coach's knowledge base is thin on that topic, that's a gap worth filling. If users are asking about things the coach isn't designed for, consider whether you need clearer guidance on scope or a new coach for that topic.