Boolean Search & AI Sourcing

If you already know boolean, skip ahead. If it's always been a bit fuzzy, here's the quick version:

AND — Both terms must appear. `"product manager" AND "B2B SaaS"` finds people who have both on their profile.

OR — Either term can appear. `"product manager" OR "product owner"` captures both title variations.

NOT — Excludes a term. `"product manager" NOT "junior"` removes junior-level results.

Quotation marks — Exact phrase. `"senior product manager"` finds that exact title, not just pages containing "senior" and "product" and "manager" scattered around.

Parentheses — Group terms. `("product manager" OR "product owner") AND ("B2B" OR "SaaS")` combines groups logically.

That's it. Five operators. But combining them into effective, comprehensive searches for complex roles? That's where AI shines.

Here's the prompt that will change how you source:

"I'm hiring a Senior Product Manager for a B2B SaaS company. They need experience with enterprise sales cycles, product-led growth, and managing a team. Generate a comprehensive boolean search string for LinkedIn Recruiter that includes: title variations, skill variations, and industry context. Include synonyms and related terms I might not have thought of."

AI doesn't just translate your brief into boolean. It expands your search by including synonyms, alternative titles, and related terms you'd never think of on your own.

For a Senior Product Manager, AI might generate:

`("Senior Product Manager" OR "Lead Product Manager" OR "Principal Product Manager" OR "Head of Product" OR "Director of Product" OR "Group Product Manager" OR "Sr PM" OR "Staff Product Manager") AND ("B2B" OR "enterprise" OR "SaaS" OR "software-as-a-service") AND ("product-led growth" OR "PLG" OR "user acquisition" OR "product analytics" OR "growth strategy") AND ("team lead" OR "people management" OR "managed a team" OR "direct reports" OR "team of")`

Notice how AI included "PLG" as a synonym for "product-led growth," captured title variations you might have missed like "Staff Product Manager," and used different phrasings for team management. That one prompt just made your search significantly more comprehensive.

Even faster approach — paste your entire job description into AI:

"Here's the job description for the role I'm sourcing. Generate a boolean search string for LinkedIn Recruiter based on the key requirements. Prioritise must-have skills over nice-to-haves. Include title variations and skill synonyms."

Then paste the JD. AI reads the entire specification, identifies the critical requirements, and builds the boolean string around them. This is especially valuable for technical roles where you might not know all the synonyms — AI knows that "CI/CD" also appears as "continuous integration," "continuous deployment," "DevOps pipeline," and "build automation."

Pro tip: Ask AI to generate three versions of the string — one strict (must-haves only), one moderate (must-haves plus top nice-to-haves), and one broad (casting a wide net). Start with strict. If you don't get enough results, move to moderate, then broad.

LinkedIn Recruiter is powerful but limited to LinkedIn's database. X-Ray searching uses Google to find LinkedIn profiles that don't appear in Recruiter — plus profiles on other platforms entirely.

The basic X-Ray formula:

`site:linkedin.com/in "job title" "skill" "location"`

AI makes this infinitely more sophisticated. Try:

"Generate a Google X-Ray search to find Senior DevOps Engineers in London who are currently working and have experience with AWS and Kubernetes. Include LinkedIn profiles and also search GitHub and Stack Overflow."

AI generates:

LinkedIn X-Ray: `site:linkedin.com/in "DevOps Engineer" OR "Site Reliability Engineer" OR "Platform Engineer" "London" "AWS" "Kubernetes"`

GitHub X-Ray: `site:github.com "DevOps" OR "SRE" "London" "AWS" "Kubernetes" "contributions"`

Stack Overflow: `site:stackoverflow.com/users "DevOps" "London" "AWS" "Kubernetes"`

Now you're sourcing from three platforms with searches that took 30 seconds to generate. Most recruiters only source from LinkedIn. You're already ahead.

If you're hiring developers, GitHub is a goldmine — and most recruiters ignore it because it feels intimidating.

You don't need to understand code. You need to understand signals. Ask AI:

"I'm sourcing software engineers who know Python and have contributed to open-source projects. What should I look for on a GitHub profile to assess whether someone is a strong candidate? What are the signals of an active, skilled developer?"

AI tells you:

- Green contribution graph — regular commits suggest an active developer

- Popular repositories — projects with many stars indicate respected work

- Contribution to known projects — contributing to established open-source libraries shows collaboration skills

- Code quality in pinned repos — well-documented repositories with clear READMEs suggest professionalism

- Recent activity — active in the last 3-6 months means they're still coding regularly

You don't need to read the code. You need to read the patterns.

LinkedIn and GitHub aren't the only places candidates live. AI can help you source from channels most recruiters overlook:

Stack Overflow — Developers answer technical questions here. Top answerers in specific technologies are often strong candidates.

Medium / Substack / Dev.to — People who write about their craft are usually knowledgeable and articulate. Ask AI to help you find relevant authors.

Meetup and conference speakers — Ask Perplexity to find recent speakers at conferences related to your role. Conference speakers are often senior and well-connected.

Twitter/X — Many professionals, especially in tech, product, and marketing, are active on X. AI can help you craft searches for people discussing relevant topics.

Industry Slack and Discord communities — Ask AI which communities exist for your target profession. Many have job boards or active members who might be open to opportunities.

For each channel, the AI prompt is the same pattern: "I'm sourcing [role] candidates. How can I find strong candidates on [platform]? What should I search for and what signals indicate a good candidate?"

Here's the workflow that ties it all together:

Step 1: Get the brief from the hiring manager. Understand the must-haves vs. nice-to-haves (use Day 3's approach).

Step 2: Paste the JD into AI and generate three tiers of boolean strings for LinkedIn Recruiter.

Step 3: Generate X-Ray searches for Google, GitHub, and any other relevant platforms.

Step 4: Run the strict searches first. Review the top results.

Step 5: If the strict pool is too small, move to moderate, then broad.

Step 6: For each promising profile, use AI to do a quick assessment (you'll master this tomorrow on Day 5).

This entire workflow — from brief to shortlist — takes about 30 minutes for most roles. Compare that to the hours of manual searching and you start to see why AI-powered recruiters consistently outperform.