pipeline-output: accessibility, precision, and series closer

Adds an inline gloss for SingleTurnSample where readers first encounter it,
names EvaluationResult precisely as the return type of evaluate(), and
normalizes "golden query set" to "golden set" in the intro to match the
rest of the tutorial. Ends the tutorial with a Wrapping Up section that
closes the series.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output.md

@@ -11,7 +11,7 @@ aliases:
 |--------------|---------------------|--|----|
 
 This tutorial focuses on **pipeline output quality**: whether the full retrieval pipeline produces the right output once retrieved results reach a consumer, most often an LLM generator in a RAG system.
-To measure pipeline output quality, you run your golden query set through the full pipeline, capture each `(question, retrieved_context, answer)` triple, and score the triples against judgment metrics like faithfulness, answer relevancy, and context precision.
+To measure pipeline output quality, you run your golden set through the full pipeline, capture each `(question, retrieved_context, answer)` triple, and score the triples against judgment metrics like faithfulness, answer relevancy, and context precision.
 
 For orientation on the four layers of retrieval evaluation and where this tutorial fits, see [Measuring ANN Precision](/documentation/tutorials-search-engineering/retrieval-quality/#the-four-layers-of-retrieval-evaluation).
 
@@ -53,7 +53,7 @@ Question:
 """
 ```
 
-**3. Run retrieval and generation.** For each entry, retrieve the top-k chunks, pass them through the generator, and record a `SingleTurnSample`. The example uses Anthropic, but any LLM provider works (OpenAI, Cohere, a local model). Only the `generate_answer` body changes:
+**3. Run retrieval and generation.** For each entry, retrieve the top-k chunks, pass them through the generator, and record a `SingleTurnSample`. `SingleTurnSample` is Ragas's data class for one evaluation record: question, retrieved context, generated answer, and optional reference. The example uses Anthropic, but any LLM provider works (OpenAI, Cohere, a local model). Only the `generate_answer` body changes:
 
 ```python
 import os
@@ -128,7 +128,7 @@ scores = evaluate(
 )
 ```
 
-`evaluate()` returns a result object whose aggregate scores look like this:
+`evaluate()` returns an `EvaluationResult` object. Its aggregate scores print like this:
 
 ```python
 {"faithfulness": 0.88, "answer_relevancy": 0.81, "context_precision": 0.74}
@@ -198,3 +198,7 @@ Pair offline metric changes with online KPI changes over a few launches. After t
 ### Proxy Signals When You Can't A/B
 
 Most teams don't have a proper experimentation platform, or don't have the traffic to power a test quickly. Cheap-to-instrument signals correlate enough with value to catch big regressions and big wins: thumbs up or down on answers, regeneration rate, source-click rate, copy or share actions, and session abandonment. Build these into production telemetry before you need them.
+
+## Wrapping Up
+
+That completes the four-layer retrieval evaluation stack: ANN precision, retrieval relevance, pipeline output quality, and business impact. Each layer catches a different class of regression, and running them together means retrieval changes can ship with defensible evidence at every stage.