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Engineering

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 Step

Select 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 Step

Auto-generate 50 diverse test inputs from the recipe's input schema, covering a range of scenarios and edge cases.

Run Multi-Judge Evaluation

Parallel

Execute all model variants in parallel, then score each output with 2-3 independent LLM judges for consensus.

Review Statistical Results

Approval Gate

Engineering lead reviews confidence intervals, cost comparison, and inter-judge agreement before promoting the winning model.

See the Engineering workflow in action

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

Install the Engineering Pack to get this workflow and more, ready to run.