AI with Michal

Boolean search

Literal keyword logic (AND, OR, NOT, parentheses, quotes) you use to narrow a talent pool in databases, job boards, or LinkedIn before you lean on semantic or AI ranking.

Michal Juhas · Last reviewed May 2, 2026

Who this is for

Sourcers and recruiters who work in lists (CSV, CRM, LinkedIn Recruiter, data vendors) and need reproducible strings the team can share and tweak.

In practice

  • Anchor on must-haves: title OR blocks, location, language, and one proof signal (cert, stack, domain).
  • Use NOT for poison pills: agencies you cannot hire from, irrelevant industries, junior-only noise when you need senior scope.
  • Version your strings: save v1, v2, and what each change did to volume and quality so you can roll back.

Where it breaks

Title chaos, non-English markets, and stealth startups with sparse profiles defeat naive AND stacks. If your Boolean returns zero or fifty thousand, the query is lying about how well you understand the market.

From recent workshops

In sourcing automation workshops we spend time on APIs and data providers before deep manual work on LinkedIn. Boolean still matters there because those tools give you structured fields to filter on. Start with clear filters, then refine.

Boolean versus semantic shortlists

ApproachStrengthWeak spot
BooleanHard exclusions, auditabilitySynonyms and fuzzy titles
Semantic / vectorMeaning and similar phrasingHarder to explain "why this row" to compliance
HybridBoolean slice, semantic rankNeeds clear owner for each step

Related on this site

Frequently asked questions

When is Boolean search still the right first move?
When you have crisp tokens (company names, certifications, exact titles, locations) and need deterministic filters. In sourcing workshops we often treat Boolean plus provider filters as the spine of a search, then add semantic or AI layers for ranking or rewrites.
Why do workshops put LinkedIn later in the flow?
Because scraping or enriching at scale has policy and rate limits. Teams often start in licensed data providers or internal CRM exports, apply Boolean there, then use LinkedIn for human verification or last-mile outreach. The order saves time and reduces brittle automation.
What is the main failure mode of Boolean-only sourcing?
Missed synonyms and title inflation: the same role might read "Head of Talent" in one market and "TA Lead" in another. Pure Boolean rewards exact wording; pair it with synonym lists, OR blocks, or semantic search when language drifts.
How does Boolean interact with AI sourcing?
Boolean is precise; semantic search and LLMs handle meaning and paraphrase. A practical split: Boolean for must-haves and exclusions, AI for shortlists, outreach drafts, and explaining fit. See Boolean search vs AI sourcing.
Can I teach Boolean to non-sourcers?
Yes if you give templates and examples, not abstract logic lessons. Few-shot prompting works the same way: show three good strings, then ask for variations. The Starting with AI: the foundations in recruiting course walks recruiters through that pattern.
Where can I practice with real recruiting stacks?
Browse AI sourcing tools for recruiters and the tools directory. Pair reading with a live workshop so you can compare provider quirks with peers.
Does Boolean replace GDPR or compliance thinking?
No. Boolean only shapes queries. Storage, retention, and lawful basis still depend on your ATS, spreadsheets, and automation choices. If you automate exports, document why each field exists.

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