- Why Boolean limits the pool of candidates you actually find
- How semantic search reads intent, not just keywords
- A 5-step process to convert any role brief into a working AI search
- Three real examples you can copy today
Boolean searches were built for databases. You type the words, the database matches the words. It works fine if every great candidate uses the same words on their profile. But they do not.
The candidate you actually want might describe themselves as a “product engineer” while you typed “full-stack developer”. They might say “led migration to microservices” while you typed “distributed systems”. Boolean misses both. AI sourcing does not.
What semantic search actually does
Semantic search reads intent. You describe the role the way you would explain it to a friend, and the AI finds people whose history matches the meaning, not just the words. No syntax. No operators. No wildcards.
("backend engineer" OR "backend developer") AND (Rust OR Go) AND (payments OR fintech) AND (Berlin OR Munich)New way: Senior backend engineer in Berlin, 5+ years in Rust or Go, has shipped at a payments company.
The 5-step process
Most teams get a role brief and immediately start typing into LinkedIn Recruiter. Stop. Spend 10 minutes here first and your shortlist will be 3x better.
- Write the brief in plain English. Two sentences max. What does this person do, and what have they done before?
- List the must-haves and the deal-breakers. Three to five of each. No more.
- Add the soft signals. Worked at a company under 200 people? Has shipped to production? Has led a team?
- Run the search. Ranked, with reasoning per candidate.
- Check the bottom of the shortlist. The 8th-best candidate often teaches you something the top of the list does not.
Three real searches you can copy
Senior backend engineer for a fintech in Berlin
Senior backend engineer in Berlin or remote in EU. 5+ years in Rust or Go. Has shipped at a payments or banking company. Has led a small team. Comfortable with ambiguity, ships fast.
VP of People for a Series B SaaS
VP People for a B2B SaaS, EU-based, has scaled a team from 50 to 200. Comfortable with HR ops AND talent strategy. Has implemented an HRIS migration. Bonus if they have built compensation bands.
Forward-deployed engineer
Forward-deployed engineer, ex-startup, NYC. Comfortable in front of customers. Ships in days not weeks. Has worked on AI or ML products before.
The mistake most recruiters make
They write the search like a job description. JD-style searches return JD-style profiles. Real candidates do not write their LinkedIn like a JD. Write the search the way you would describe a great person you met at a dinner party.
What to do when the AI gets it wrong
Sometimes the top result is wrong. That is fine. Click into the candidate, look at the AI’s reasoning, and tell it what to weight differently. Most modern sourcing tools let you give feedback per candidate, and the next search is better. Treat the AI like a junior recruiter who is learning your taste.
The bottom line
Boolean is fine for a quick pull. Semantic search is the way to find the candidates Boolean misses. Spend 10 minutes writing a clean brief and you will fill more roles, faster, with less frustration.