json formatting + title matching

pages/applications/_meta.en.json

@@ -1,5 +1,5 @@
 {
-  "pal": "Program-Aided Language Models",
-  "generating": "Generating Data",
-  "workplace_casestudy": "Graduate Job Classifcation Case Study"
+    "pal": "Program-Aided Language Models",
+    "generating": "Generating Data",
+    "workplace_casestudy": "Graduate Job Classification Case Study"
 }

pages/applications/workplace_casestudy.en.mdx

@@ -1,4 +1,4 @@
-# LLMs as Graduate Job Classifiers Case-Study
+# Graduate Job Classification Case Study
 
 [Clavié et al., 2023](https://arxiv.org/abs/2303.07142) provide a case-study on prompt-engineering applied to a medium-scale text classification use-case in a production system. Using the task of classifying whether a job is a true "entry-level job", suitable for a recent graduate, or not, they evaluated a series of prompt engineering techniques and report their results using GPT-3.5 (`gpt-3.5-turbo`).