ann-precision: add Prerequisites block and rename closing to Next Steps

Adds an explicit Prerequisites line matching the pattern in Measuring
Retrieval Relevance and Evaluating Pipeline Output Quality. Renames
"Wrapping Up" to "Next Steps" for naming consistency across the series.

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

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

@@ -14,6 +14,8 @@ weight: 5
 This tutorial focuses on **ANN precision**: how closely approximate nearest-neighbor (ANN) search matches exact kNN search.
 To measure ANN precision, you compare Qdrant's approximate top-k against the exact kNN top-k using `precision@k`, then tune HNSW parameters to trade memory and build time for higher precision.
 
+**Prerequisites.** A Qdrant collection with your corpus indexed. For the CI section, Python with `qdrant-client` installed.
+
 ## The Four Layers of Retrieval Evaluation
 
 Retrieval quality operates at four layers. Each catches different failure modes at a different cadence and cost. This tutorial covers layer 1.
@@ -89,7 +91,7 @@ def avg_precision_at_k(
 
 Wire it into CI and fail the job when precision falls below your target threshold. This catches regressions from embedding model swaps or index config changes before they reach production.
 
-## Wrapping Up
+## Next Steps
 
 Measuring ANN precision keeps HNSW tuning honest. The Search Quality tab gives you a quick interactive read; the Python helper plugs into CI to catch regressions after embedding model changes or index config updates.