RAG Eval Playground: Building a Real Evaluation Loop Locally
How to use the RAG Eval Playground starter to move from anecdotal prompting to measurable retrieval and answer quality.
Why This Starter Exists
RAG systems often fail silently. They look fine in demos, then miss critical context in production.
The examples/starters/rag-eval-playground/ starter exists to prevent that by making evaluation part of normal development instead of a late-stage check.
What The Starter Gives You
The starter includes:
- a dedicated
config.yaml - an evaluation script (
evaluate.py) - a focused README for repeatable setup
That simple structure encourages a feedback cycle: ingest -> query -> score -> adjust -> repeat.
Metrics Before Opinions
A practical local workflow should track at least:
- retrieval relevance
- answer groundedness
- answer completeness
Even rough baseline metrics are better than confidence built from a few successful prompts.
Local-First Advantage
Running the loop locally has a hidden advantage: faster iteration on chunking, embeddings, and prompt templates without waiting on external infra changes.
That makes it easier to isolate whether poor quality comes from retrieval, prompt construction, or model behavior.
How I Use It With Other Starters
I usually pair this starter with:
local-rag-studiofor baseline behaviorhybrid-cache-rag-assistantwhen latency tuning begins
This creates a pattern where one starter explores behavior and another validates quality under change.
Practical Takeaway
The best time to add evaluation is when your first retrieval pipeline works, not when stakeholders ask why answers drifted.
The RAG Eval Playground gives a small but effective structure for that discipline.