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news_tagging_schema.txt

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+{
+    "properties": {
+        "topic": {
+            "type": "string",
+            "enum": ["science", "sports", "politics", "weather", "crime", "entertainment", "religion", "travel", "business"],
+            "description": "what topic this news article is about"
+        },
+        "language": {
+            "type": "string",
+            "enum": ["spanish", "english", "french", "german", "italian", "hindi"]
+        }
+    },
+    "required": ["topic", "language"]
+}

restaurant_review_prompt.txt

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+Analyze the following restaurant review, and extract information from it: <review> {input_text} </review>. Output should contain the following fields in json:
+
+1. overall 'sentiment' of the review (positive/neutral/negative),
+2. 'summary' of the review in 50 words or less
+2. 'key points' of the review as an array (maximum 3)
+2. 'food rating' on a scale of 1 to 5 with 1 being pathetic food and 5 being super awesome food,
+3. 'ambience rating' on a scale of 1 to 5 with 1 being poor ambience (eg small tables, poor lighting etc) and 5 being excellent ambience,
+4. 'service rating' on a scale of 1 to 5 with 1 being pathetic service and 5 being wow service,
+5. 'hygiene rating' on a scale of 1 to 5, with 1 being poor hygiene that will cause sickness and 5 being squeaky clean
+6. 'recommended dishes' as an array,
+7. 'dishes not recommended' as an array,
+8. 'restaurant name',
+9. 'restaurant cuisine'
+
+For any of the above, please output "NA" if you feel there isn't enough information in the review to say for sure.

resume_extraction_schema.txt

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+{
+    "properties": {
+        "name": {"type": "string"},
+        "email_address": {"type": "string"},
+        "phone_number": {"type": "string"},
+        "education": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "area_of_education": {"type": "string"},
+                    "degree_awarded": {"type": "string"},
+                    "school": {"type": "string"},
+                    "year_of_graduation": {"type": "string"}
+                }
+            }
+        },
+        "prior_work_experience": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "company_name": {"type": "string"},
+                    "position": {"type": "string"},
+                    "start_date": {"type": "string"},
+                    "end_date": {"type": "string"}
+                }
+            }
+        }
+    }
+}