Choosing the Right LLM with Bakeoffs
Systematically evaluate which model produces the best output for a given recipe.
The Problem
Teams pick an LLM model based on intuition or marketing claims, then stick with it indefinitely. When new models launch, nobody runs a rigorous comparison — so teams either miss better options or switch prematurely based on hype. The result is suboptimal quality, unnecessary cost, or both.
The Solution
JieGou's bakeoff system runs the same inputs through multiple model configurations and uses LLM-as-judge scoring to determine which model actually performs best. Statistical confidence intervals prevent premature conclusions, and synthetic input generation ensures a diverse test set.
Workflow Steps
Create Bakeoff
Recipe StepSelect the recipe to evaluate and choose two or more model configurations to compare (e.g., Claude Sonnet vs. GPT-5 vs. Gemini Pro).
Generate Synthetic Inputs
Recipe StepAuto-generate 50 diverse test inputs from the recipe's input schema, covering a range of scenarios and edge cases.
Run Multi-Judge Evaluation
ParallelExecute all model variants in parallel, then score each output with 2-3 independent LLM judges for consensus.
Review Statistical Results
Approval GateEngineering lead reviews confidence intervals, cost comparison, and inter-judge agreement before promoting the winning model.
Expected Outcomes
- Data-driven model selection instead of guesswork
- Cost optimization — cheaper models that match quality are identified
- Statistical confidence prevents premature conclusions
- Repeatable process for re-evaluation when new models launch
Try this workflow
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