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.
Journey overview
How this pipeline works
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use H2O.ai to inputs, context, and settings are ready so the workflow can move into execution without blockers. Then, you pass the output to BLACKBOX AI to supporting assets from llm integration are prepared and connected to the main workflow. Then, you pass the output to Catalyst to supporting assets from model evaluation are prepared and connected to the main workflow. Then, you pass the output to Fiddler AI to a first-pass final deliverable is generated and ready for refinement in the next steps. Then, you pass the output to Kodaps to the final deliverable is improved, validated, and prepared for final delivery. Then, you pass the output to InsightAI Sheets to the final deliverable is improved, validated, and prepared for final delivery. Finally, a specialized tool is used to a finalized final deliverable is ready for publishing, handoff, or integration.
A finalized final deliverable is ready for publishing, handoff, or integration.
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Prepare inputs and settings through LLM Fine-tuning before running llm evaluation.
LLM Fine-tuning sets up the foundation for llm evaluation; clean inputs here reduce downstream rework.
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Use LLM Integration to build supporting assets that improve llm evaluation quality.
LLM Integration strengthens llm evaluation by feeding better supporting material into the pipeline.
Supporting assets from llm integration are prepared and connected to the main workflow.
Use Model Evaluation to build supporting assets that improve llm evaluation quality.
Model Evaluation strengthens llm evaluation by feeding better supporting material into the pipeline.
Supporting assets from model evaluation are prepared and connected to the main workflow.
Execute llm evaluation with LLM evaluation to produce the primary final deliverable.
This is the core step where llm evaluation actually happens, so it determines baseline quality for everything after it.
A first-pass final deliverable is generated and ready for refinement in the next steps.
Refine and validate llm evaluation output using LLM Orchestration before final delivery.
LLM Orchestration adds quality control so issues are caught before the workflow is finalized.
The final deliverable is improved, validated, and prepared for final delivery.
Refine and validate llm evaluation output using Orchestrate LLM workflows before final delivery.
Orchestrate LLM workflows adds quality control so issues are caught before the workflow is finalized.
The final deliverable is improved, validated, and prepared for final delivery.
Package and ship the output through LLM-as-a-Judge & Human-in-the-Loop so llm evaluation reaches end users.
LLM-as-a-Judge & Human-in-the-Loop is what turns intermediate output into a usable, publishable result for real users.
A finalized final deliverable is ready for publishing, handoff, or integration.
Start this workflow
Ready to run?
Follow each step in order. Use the top pick for each stage, then compare alternatives.
Begin Step 1Time to first output
30-90 minutes
Includes setup plus initial result generation
Expected spend band
Free to start
You can swap tools by pricing and policy requirements
Delivery outcome
A finalized final deliverable is ready for publishing, handoff, or integration.
Use each step output as the input for the next stage
Why this setup
Repeatable process
Structured so any team can repeat this workflow without starting over.
Faster tool selection
Each step recommends the best tool to reduce trial-and-error.
Quick answers to help you decide whether this workflow fits your current goal and team setup.
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
Continue with adjacent playbooks in the same domain.
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