diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 2c226411b0c58bcbac04e0c866f4eab09a44a9a7..1d12394a190fad17d5c7b5eeac65a8645819e756 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -801,12 +801,12 @@ turning on some experimental options.
 
 ## Caching Data In Memory
 
-Spark SQL can cache tables using an in-memory columnar format by calling `cacheTable("tableName")`.
+Spark SQL can cache tables using an in-memory columnar format by calling `sqlContext.cacheTable("tableName")`.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
-memory usage and GC pressure. You can call `uncacheTable("tableName")` to remove the table from memory.
+memory usage and GC pressure. You can call `sqlContext.uncacheTable("tableName")` to remove the table from memory.
 
-Note that if you call `cache` rather than `cacheTable`, tables will _not_ be cached using
-the in-memory columnar format, and therefore `cacheTable` is strongly recommended for this use case.
+Note that if you call `schemaRDD.cache()` rather than `sqlContext.cacheTable(...)`, tables will _not_ be cached using
+the in-memory columnar format, and therefore `sqlContext.cacheTable(...)` is strongly recommended for this use case.
 
 Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running
 `SET key=value` commands using SQL.