Who should use the LLM evaluation workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
7-step pipeline — follow each step in order
1Setup · Step 1 of 7
Open task pageLLM Fine-tuning
Prepare inputs and settings through LLM Fine-tuning before running llm evaluation.
Why this matters
LLM Fine-tuning sets up the foundation for llm evaluation; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
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2Input · Step 2 of 7
Open task pageLLM Integration
Use LLM Integration to build supporting assets that improve llm evaluation quality.
Why this matters
LLM Integration strengthens llm evaluation by feeding better supporting material into the pipeline.
What you get
Supporting assets from llm integration are prepared and connected to the main workflow.
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3Input · Step 3 of 7
Open task pageModel Evaluation
Use Model Evaluation to build supporting assets that improve llm evaluation quality.
Why this matters
Model Evaluation strengthens llm evaluation by feeding better supporting material into the pipeline.
What you get
Supporting assets from model evaluation are prepared and connected to the main workflow.
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4Core · Step 4 of 7
Open task pageLLM evaluation
Execute llm evaluation with LLM evaluation to produce the primary final deliverable.
Why this matters
This is the core step where llm evaluation actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass final deliverable is generated and ready for refinement in the next steps.
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5Refine · Step 5 of 7
Open task pageLLM Orchestration
Refine and validate llm evaluation output using LLM Orchestration before final delivery.
Why this matters
LLM Orchestration adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
6Refine · Step 6 of 7
Open task pageOrchestrate LLM workflows
Refine and validate llm evaluation output using Orchestrate LLM workflows before final delivery.
Why this matters
Orchestrate LLM workflows adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
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7Deliver · Step 7 of 7
Open task pageLLM-as-a-Judge & Human-in-the-Loop
Package and ship the output through LLM-as-a-Judge & Human-in-the-Loop so llm evaluation reaches end users.
Why this matters
LLM-as-a-Judge & Human-in-the-Loop is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized final deliverable is ready for publishing, handoff, or integration.