Update src/app_job_copy_1.py

src/app_job_copy_1.py

@@ -155,7 +155,7 @@ def setup_llm():
     sum_llm = llm.with_structured_output(Shortlist)
     
     # Create system prompt
-    system = """You are an expert Recruitor, your task is to analyse the Candidate profile and determine if it matches with the job details and provide a score(out of 10) indicating how compatible the
+    system = """You are an expert Tech Recruitor, your task is to analyse the Candidate profile and determine if it matches with the job details and provide a score(out of 10) indicating how compatible the
     the profile is according to job.
 Try to ensure following points while estimating the candidate's fit score:
 For education:
@@ -166,7 +166,9 @@ Tier3 - Unknown or unranked institutions - Lower points or reject
 
 Startup Experience Requirement:
 Candidates must have worked  as a direct employee at a VC-backed startup (Seed to series C/D)
-preferred - Y Combinator, Sequoia,a16z,Accel,Founders Fund,LightSpeed,Greylock,Benchmark,Index Ventures,etc.   
+preferred - Y Combinator, Sequoia,a16z,Accel,Founders Fund,LightSpeed,Greylock,Benchmark,Index Ventures,etc. 
+
+Apart from this the candidate must reside near or on the job location. If it is not immediately give a fit score below 5.
 
     The fit score signifies based on following metrics:
     1–5 - Poor Fit - Auto-reject
@@ -174,7 +176,7 @@ preferred - Y Combinator, Sequoia,a16z,Accel,Founders Fund,LightSpeed,Greylock,B
     8.0–8.7 - Moderate Fit - Auto-reject
     8.8–10 - STRONG Fit - Include in results
 
-    Each candidate's fit score should be calculated based on a weighted evaluation of their background and **must be distinct even if candidates have similar profiles**. You may use slight variations to reflect nuanced differences.
+    Each candidate's fit score should be calculated based on a weighted evaluation of their background and must be distinct even if candidates have similar profiles.
     """
     
     # Create query prompt