Experimentation & Rapid Iteration
The Prompt Playground provides a dedicated environment for testing and refining prompts:- Multi-model testing: Experiment across open-source, closed, and custom models, adjusting parameters like temperature and max tokens
- Side-by-side comparison: Compare up to five prompts or versions simultaneously, analyzing outputs, latency, cost, and token usage
- Tool and MCP integration: Attach tools (API, code, or schema) and connect MCP servers to test agentic workflows
- RAG pipeline testing: Connect your retrieval pipeline via Context Sources to test prompts with real retrieved context
- Session management: Save, tag, and recall full playground states, including variable values and conversation history, so you never lose an experiment
Organization & Governance
Treat prompts as managed assets with a centralized Prompt CMS:- Prompt versioning: Every change is tracked with author and timestamp. Publish versions, view diffs between them, and roll back to a known-good state if needed
- Prompt Partials: Create reusable snippets for recurring instructions (safety guidelines, output formats, etc.) and inject them using simple syntax like
{{partials.tone-and-structure.latest}}. This ensures teams use standardized, approved language across prompts - Folders and tags: Organize prompts systematically as your library grows
- Role-based access control: Control who can view, edit, or deploy prompts at the workspace level
Evaluation & Quality Measurement
Move beyond manual testing with systematic, data-driven evaluation:- Dataset-driven testing: Run prompts against datasets of test cases with evaluators attached to measure accuracy, toxicity, relevance, and custom criteria
- Comparison reports: Compare prompt versions at scale, analyzing scores across all test cases to make informed decisions and prevent regressions
- Tool call accuracy: Measure whether your prompts select the correct tools with correct arguments for agentic workflows
- Retrieval quality: Evaluate context precision, recall, and relevance to identify and fix RAG pipeline issues
- Human-in-the-loop: Set up annotation pipelines for qualitative review alongside automated evaluators
Optimization & Deployment
Close the loop from experimentation to production:- AI-powered optimization: Use Maxim’s prompt optimizer to automatically analyze test results and generate improved prompt versions with detailed reasoning for each change
- Decoupled deployment: Deploy prompts directly from the UI with deployment variables, no code changes required. Product teams can push updates without waiting on engineering
- SDK integration: Query prompts programmatically via the Maxim SDK to dynamically retrieve version-controlled prompts in production