Baserun helps AI teams build, monitor and iterate their LLM applications. At a high level, the LLM application development lifecycle can be broken down into three phases: build, monitor, and improve. Each phase has its unique challenges, as shown in the following image:Documentation Index
Fetch the complete documentation index at: https://docs.baserun.ai/llms.txt
Use this file to discover all available pages before exploring further.
| Tasks | Feature name |
|---|---|
| Configure Model | Prompt Playground (UI) Compare Prompt Feature (UI) Monitoring > Log LLM requests (SDK + UI) |
| Prototype workflow | Monitoring > Trace multi-step workflow (SDK + UI) |
| Monitor App Performance | Monitoring > Log LLM requests (SDK + UI) |
| Monitor User Experience | Monitoring Features (SDK + UI) - Log LLM Requests - Trace Multi-Step Workflow - Users and User Sessions - Collect User Feedback Annotation > Checks (SDK + UI) |
| Debug workflows | Monitoring Features (SDK + UI) - Log LLM Requests - Trace Multi-Step Workflow - Users and User Sessions |
| Create datasets | Coming soon |
| Prompt Engineering | Monitoring > Trace multi-step workflow (SDK + UI) Prompt playground (UI) Compare prompt feature (UI) |
| Context Engineering (RAG) | Coming soon |
| Fine-tuning | Coming soon |
| Workflow Optimization | Monitoring > Trace multi-step workflow (SDK + UI) |
Getting Started
Prompt playground overview
Iterating, versioning, and testing prompts.
Logging LLM requests
Start logging your LLM requests with 2 lines of code changes.
Tracing End-to-end LLM Pipelines
Debugging and analyzing complex LLM pipelines.