Back to Prompt Library
implementation
LMDeploy Model Integration & Serving
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: Hybrid Reasoning AI Evaluation Engine
Format
Text-first
Lines
1
Sections
1
Linked challenge
Hybrid Reasoning AI Evaluation Engine
Prompt source
Original prompt text with formatting preserved for inspection.
1 lines
1 sections
No variables
0 checklist items
Set up LMDeploy to serve at least two different LLMs (e.g., OpenAI o3 and a Llama variant). Implement the logic within your evaluation engine to dynamically switch between these models for benchmarking. Design the MCP-enabled tool to feed model outputs from LMDeploy into the DSPy evaluation pipeline.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.