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Create a reusable skill for evaluating fine-tuned models, benchmarking performance, and detecting quality regressions
Passed 19 of 21 test cases with the skill, and 12 of 21 without it.
and 6 more in the full report
7 fixed13 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is the recommended deployment threshold percentage for accuracy on a task-specific benchmark when evaluating fine-tuned models?
| Checked | Without skill | With skill |
|---|---|---|
| Requires task-specific accuracy greater than 85%. | Missed | Passed |
The instructions your model reads when the skill loads.
Before learning about model evaluation, you will build the skill that captures that knowledge. This skill-first approach means every concept you learn gets encoded into a reusable asset that becomes part of your Digital FTE toolkit.
When you fine-tune a model, how do you know it actually improved? A model might generate fluent text that completely misses the point. Evaluation frameworks provide systematic methods to measure what matters: accuracy, format compliance, reasoning quality, and safety. By the end of this chapter, you will have a skill that guides evaluation decisions for any fine-tuned model.
Every chapter starts with a clean environment. This prevents state pollution from previous work and ensures reproducible results.
# Navigate to your workspace
cd ~/workspace
# Clone fresh skills-lab (or reset if exists)
if [ -d "skills-lab-llmops" ]; then
rm -rf skills-lab-llmops
fi
git clone https://github.com/panaversity/skills-lab.git skills-lab-llmops
cd skills-lab-llmops
# Create chapter directory
mkdir -p llmops-evaluation
cd llmops-evaluation
Output:
Cloning into 'skills-lab-llmops'...
remote: Enumerating objects: 156, done.
remote: Counting objects: 100% (156/156), done.
Receiving objects: 100% (156/156), 45.23 KiB | 2.26 MiB/s, done.
Before fetching documentation, articulate what you want to learn. This specification drives focused learning.
Create LEARNING-SPEC.md:
test cases passed, vs 12 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.