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src/prompts/sourcing.py

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+"""
+LLM prompt template for candidate sourcing via Google X-ray LinkedIn search.
+
+Generates optimized boolean search queries for finding matching candidates
+on LinkedIn through Google search (site:linkedin.com/in).
+"""
+
+XRAY_QUERY_GENERATION_PROMPT = """You are an expert technical recruiter specializing in sourcing candidates via Google X-ray LinkedIn search. Given a job description and context, generate optimized search queries to find matching candidates on LinkedIn.
+
+JOB DESCRIPTION:
+{job_description}
+
+CONTEXT:
+- Location: {location}
+- Industry: {industry}
+- Compensation Band: {compensation_band}
+- Company Stage: {company_stage}
+
+YOUR TASK:
+
+1. ANALYZE the JD and extract searchable keywords: job titles, skills, tools, certifications, companies, institutes, and seniority indicators.
+
+2. GENERATE 5-8 Google X-ray search queries using `site:linkedin.com/in` syntax. Each query should target a different sourcing angle:
+   - Broad title + location match
+   - Specific skills combination
+   - Competitor/similar company alumni
+   - Certification or credential holders
+   - Educational institution alumni
+   - Industry-specific terminology
+   - Seniority-level targeting
+   - Niche or long-tail keyword combinations
+
+3. GENERATE 3-5 LinkedIn boolean search strings (for use in LinkedIn's search bar directly, no site: prefix).
+
+4. PROVIDE 3-5 actionable sourcing tips specific to this role and market.
+
+INDIA-SPECIFIC GUIDANCE:
+- Use Indian city names with common variants (e.g., "Bangalore" OR "Bengaluru", "Mumbai" OR "Bombay")
+- Include major Indian IT/consulting companies: TCS, Infosys, Wipro, HCL, Cognizant, Tech Mahindra, Accenture India, Deloitte India, EY India, KPMG India, PwC India, McKinsey India, BCG India
+- Reference premier Indian institutes: IIT, IIM, BITS Pilani, NIT, ISB, XLRI, Delhi University, Anna University
+- Use Indian compensation terminology when relevant (LPA = Lakhs Per Annum)
+- Consider Indian job title conventions (e.g., "Senior Consultant" vs "Lead Consultant", "AVP" vs "Associate Director")
+
+QUERY CONSTRUCTION RULES:
+- Always start X-ray queries with: site:linkedin.com/in
+- Use double quotes for exact phrases: "senior consultant"
+- Use OR for alternatives: ("Bangalore" OR "Bengaluru")
+- Use parentheses for grouping: ("AWS" OR "Azure" OR "GCP")
+- Use minus to exclude: -recruiter -staffing
+- Keep queries under 200 characters for Google compatibility
+- Each query should be meaningfully different, not just minor variations
+
+OUTPUT THIS EXACT JSON:
+
+{{
+  "analysis": {{
+    "extracted_titles": ["list of relevant job titles to search"],
+    "key_skills": ["top 8-10 searchable technical/domain skills"],
+    "target_companies": ["8-10 companies where similar candidates work"],
+    "target_institutes": ["relevant educational institutions"],
+    "certifications": ["relevant certifications to search for"],
+    "seniority_indicators": ["terms indicating right experience level"]
+  }},
+  "queries": [
+    {{
+      "name": "Short descriptive name (e.g., Broad title + location)",
+      "strategy": "1-2 sentence explanation of what this query targets",
+      "query": "site:linkedin.com/in ... the full Google search query",
+      "expected_results": "What kind of profiles this should surface"
+    }}
+  ],
+  "boolean_strings": [
+    {{
+      "name": "Short descriptive name",
+      "purpose": "What this boolean string targets",
+      "string": "The LinkedIn boolean search string (no site: prefix)"
+    }}
+  ],
+  "sourcing_tips": [
+    "Actionable tip specific to this role and market"
+  ]
+}}
+
+IMPORTANT:
+- Queries must be copy-paste ready for Google search
+- Boolean strings must be ready for LinkedIn search bar
+- Every query should find DIFFERENT candidate pools, not the same people
+- Prioritize queries that would find passive candidates (currently employed, not actively looking)
+- Think about what someone with this profile would actually write on their LinkedIn
+"""