Bing Orchestrator Helps Search and LLMs Talk

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Search engines and LLMs work on very different principles. Tech companies have tried to marry the two, but integration is challenging.

I have just watched this video (recommended!) about Bing that sheds quite a bit of light on how the search engine and LLM integration works in Bing. The pic above is from the video and shows a diagram of the search engine-AI interaction ruled by the Orchestrator.

The “Sydney Orchestrator” (Or Bing Orchestrator) in Bing integrates ChatGPT-like AI into Microsoft’s search engine, enhancing Bing’s capabilities with GPT. Here’s how it works:

Query Processing and Iteration: There is “prompt chaining” in the background. The Orchestrator processes search queries iteratively. It generates internal queries, refining them each time to get more accurate and contextually relevant search results.

Information Grounding and Relevance: “Grounding” is to deal with AI hallucinations. The Orchestrator ensures that responses are relevant and current, crucial for recent or time-sensitive queries. It also generates iterative queries, broadening the context for the GPT model and providing a more comprehensive understanding of the subject matter. — The “precise” search option in Bing is for grounding.

Coordination Between GPT and Bing’s Indexing: The Orchestrator is an intermediary between GPT’s language processing and Bing’s search indexing.

Join us this week for the always-popular, constantly updated class

“ChatGPT and AI for Sourcing and Recruitment”

November 15-16 (Wed-Thu), 2023.

Combining AI and Web Search in Talent Sourcing: Proceed with Caution

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GPTs is wonderful AI news. Expect even more changes!

Full disclosure: I did not write or edit the post in italics below. I created a GPT, sent it to read this blog, and then asked to write a post related to AI and talent sourcing.

The GPT called itself “Blog Composer,” provided me with the picture above and wrote a post, entitling it

Combining AI and Web Search in Talent Sourcing: Proceed with Caution

Google’s latest tweak, pushing terms in quotes into snippets, is a boon for researchers. But when we integrate AI into talent sourcing, it’s a different ball game.

I recently tried using AI to sharpen my search strings. While it did refine the terms, I noticed a pattern – AI often overreached, suggesting related skills that weren’t always relevant. It’s a reminder that AI can guide, but can’t replace, the nuanced understanding of a skilled Sourcer.

In sourcing, precision is key. AI’s suggestions? Take them with a grain of salt and always double-check.

I thought it did rather well given only 5 minutes we had worked together. What do you think?

Join us at the updated, always popular session

ChatGPT and AI for Sourcing and Recruitment

November 15-16 (Wed-Thu), 2023.



LinkedIn Wants Me to Pack Groceries and Learn Fluoroscopy

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Image by #Midjourney (which cannot write yet)

LinkedIn is going to lead its members in finding relevant jobs. I want to show you what I received yesterday from LinkedIn in that regard. It would be hilarious if it weren’t so sad!

Here is my profile – Recently I have been into AI drawing with Midjourney as a hobby, and added it to my profile:

At that point, I got the industry “Graphic Design” in LinkedIn’s thinking.

Now for the email:

Is this good news for me? “Graphic Designer” has been quietly converted into “Image Specialist,” which is not a Graphic Designer, and can mean many things, including Radiology.

The most interesting part! In spite of the jobs uptick, we Image Specialists apply to pack the groceries at Whole Foods. We also assist with errands at MAC Partners, but this particular job is closed. I suppose these are my job recommendations if I want to follow the majority of fellow Image Specialists:

And then, more information is added. Perhaps this is even more striking! I have the X-Ray skill, in a sense, lol. But I have not thought of learning Fluoroscopy:

Going to the first job recommendation, I got one job to apply for – Chief Technologist. Unfortunately, it is onsite in IN while I am in CA; also, I don’t match it:

The second job link returned no results – and all of a sudden I am seeing remote Recruiter jobs (which would have been appropriate to start with):

This is messed up in every step.

You Are Missing Seniority, LinkedIn

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We have learned some unexpected things about searching for LinkedIn members by Company, Company Size, and Type. It’s good to know what to do about those.

Let’s continue examining LinkedIn filters. Consider this search in LinkedIn Recruiter using operators:

  • Current Job Title = director NOT (seniority:1 OR seniority:2 OR seniority:3 OR seniority:4 OR seniority:5 OR seniority:6 OR seniority:7 OR seniority:8 OR seniority:9 OR seniority:10)
  • Years at Company > 1

The search finds people with the word director in the current job title and no Seniority assigned.

Among results, you will see members who should have a seniority value (of “Director”,) like these. But you will not find them by any level of seniority including “Director.”

Try a similar search with the word “Senior” instead of “Director” and encounter missing results again. There are 510K+ people with the word “Senior” in the current title, over 2 years at the current company, and with no assigned level of Seniority. Here is an example – the member should have a “Senior” level assigned, but he doesn’t:

Conclusion – in addition to what my previous LinkedIn search post says:

Do not use Seniority in most of your searches.

In fact, any value selection in LinkedIn people search cuts your results in half or more! I will continue the series to cover other filters.

Please join us at the expanded two-part class

How to Find Hidden LinkedIn Profiles

on November 8th and 9th (Wed-Thu), 2024. Materials are included; seating is limited.


Get Back More Google Results By Not Using More

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image created with DALL-E

Did you realize that since Google had replaced the convenient multi-page display with the "More results" button, its users have encountered fewer results? If previously we got up to ~300-450 results, now, by pressing the "More" button until there is nothing left, gets us no more than 130-140. (Searching using quotes and operators, or Verbatim does not help). We have tested that.

To get back the results you no longer see from the "official" UI, do not use the "More" button. Instead, set your preferences to 100 results per page and add these to the search URL in turn (until there are no more results):

  • &start=100
  • &start=200
  • &start=300
  • &start=400

For example,

The pages appear with different sets of up to 100 records. You can now collate them!

An even faster option is to use this script "Google Search Results Expander" from Mike Santoro (right-mouse-click/Inspect/Console/paste the script):

javascript:(function() {
var query = prompt("Enter your Google search query:");
if (query !== null) {
var base_url = "" + encodeURIComponent(query);
var urls = [
base_url + "&num=100&start=0&filter=0",
base_url + "&num=100&start=100&filter=0",
base_url + "&num=100&start=200&filter=0",
base_url + "&num=100&start=300&filter=0"
urls.forEach(function(url, index) {
setTimeout(function() {;
}, index * 2000);

Since the Google UI change, Instant Data Scraper no longer extracts all results out-of-the-box, but you can open the tool over these pages and merge the lists of results.

Note that registration is open for the 7-Day Sourcing Bootcamp that David Galley and I will hold together, starting January 2nd - register here early:

Seven-Day Sourcing Bootcamp for Recruiters - January 2024.

We are covering all aspects of sourcing, providing materials, and support for the attendees.

Feedback has been excellent and the Bootcamp has proven to be popular, so we will likely continue updating and scheduling it 2-3 times per year.

LinkedIn’s Members and Companies Disconnect

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Sourcing Summit Europe-2023 was awesome in every way – just look at all the reports on #sosueu on LinkedIn. It’s hard to describe how wonderful the event feels; you must go there!

My presentation was  called “Unscrambling LinkedIn for More Efficient Sourcing.” As I was preparing the slides, I ran into several problems with LinkedIn Recruiter search, and I want to tell you about them.

What this post is about:

TL;DR LinkedIn Recruiter does not find many people whose profiles do point to their current company pages by:

  • Company selection
  • Company size
  • Company type.

Consider this search: the negated company selection for Exxonmobil and Boolean (text) search for exxonmobil in the company field.

If you open the first profile in the results, you will see:

There is a current clickable company pointing to Exxonmobile’s LinkedIn company page. But LinkedIn fails to recognize that the member works at this company if you select it in the search dialog. This person will NOT be found by any company size or type either.

The majority of the 4K+ results for this search are also “lost souls;” only some work at Exxonmobil country offices (and at least have company sizes).

Take Ford Motor Company (depicted above with Midjourney) and you will see a similar picture. Profiles of many of its employees are not found by the company selection (or type, or size).

LinkedIn does a poor job categorizing its data. It means that Boolean rules – it does find Ford employees by the company name.

Bottom line:

  1. Search for companies not by selection but by Boolean (text).
  2. Do not use company size or type in most of your searches.

What does this mean for LinkedIn? Engineering wants us to find more profiles. They have redone the Skills search in Recruiter in a manner that makes Skills similar to keywords (and less useful). They could achieve the goal instead by closely examining their data and restoring the data connections that should be there but are not.

(I have discovered other things, along the same lines but different filters, and will post soon.)

To proceed with the promised AI “2024” search, it would be best if LinkedIn worked on better understanding their data first.

Speaking of AI, you are invited to the updated class “ChatGPT and AI for Sourcing and Recruitment” on October 18-19.

Where Is the AI Manual?

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Search on YouTube for chatgpt in 2 minutes and you will find hundreds of results. The web and Social Networks are full of “easy” explanations on how to work with the new AI.

However, many professionals, having tried, say, to create an engaging email with ChatGPT, quickly give up because of the unsatisfying quality. People are turned off by AI hallucinations as well. Indeed, AI is trained to predict the next word in a sentence, which has little to do with being truthful.

Where are the best ChatGPT manual or instructions on how to use it?

When you buy a shelf at IKEA, it comes with instructions to follow.

When you build a spaceship, there is a design to follow.

When we use software, we rely on documentation created by Technical Writers.

But there is no such thing as complete instructions for ChatGPT or any AI. It is black boxes to be investigated. Practice and sharing observations with others are essential.

There are AI “prompt builders,” in some how-to posts, but I think they are even less helpful in mastering AI than Boolean builders in learning advanced Google search. To use AI, you need to turn on your creativity.

Sourcers have been prepared to face AI as a black box because we often work with software with incomplete or wrong documentation. We approach software hands-on and figure out what to expect from it.

The largest business network search algorithm is a perfect example. My blog has many posts about it, including one on the hidden search operators. Here is how we figured out the never-documented operators: we guessed them and the arguments’ format, then tested them.

To master ChatGTP and other AIs, it is important to set your expectations and test out your theories. If AI generates a long, generic, and unimpressive email, you can work with it to perfect the message. Answers about current facts from Bing and Bard should be verified. If you do not give up and tame AIs, the results can be spectacular – and time-saving.

You can also ask ChatGPT to help write prompts for itself:


How Do You Learn to Write Efficient Prompts for ChatGPT?

  • Unlike a kitchen gadget or pre-AI software, ChatGPT has no instructions
  • Practice is vital – experiment!
  • Teach it using examples, provide background, and correct it in a dialog
  • Debug the prompt by verifying and assessing the output
  • Learn from peers and Social Media (but question everything)
  • Learn from AI itself

We have recently delivered “ChatGPT and AI for Sourcing and Recruitment;” a recording is available, and we plan to repeat the webinar.

P.S. I have made these pics with Midjourney, an AI image creation tool. Please check out my portfolio.




We Have Compared the 7 Leading AIs

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A guest post from David Galley. By now you've heard all about how AI is "changing the game" in our - and every other -industry, replacing humans at work, or possibly ushering in the end of days. What you probably haven't heard much about is what technologies you, an individual contributor or line manager in a Talent Acquisition function, can use today that can have a positive impact on your work and workflows. This AI comparison chart is our attempt to answer that question. It is based on our extensive research:  

Please join us for an advanced ChatGPT course for Recruiters on September 13, 2023,

ChatGPT and AI for Sourcing and Recruitment.

We will go over a myriad of potential AI applications in sourcing and discuss what is reality and what is a myth.

GitHub is Paradise But Its Syntax is Jungle

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Technical Recruiters cannot afford to stay away from GitHub due to its rich data about Software Developers. It is Tech Recruiter Paradise. But GitHub user search is more of a  “Jungle.” Its syntax is incredibly complex. The documentation is helpful, yet it also contains several errors and omissions, which I will outline.

Here is an awesome list of search operators (which GitHub calls “qualifiers”) by my friends Sofia Broberger and Suzanna Frazier:

Now, let us proceed to discuss

How GitHub User Search Works

If you are just getting started, practice with the advanced search dialog – it will create the right search strings for you. But it is restrictive (same as on search engines or Indeed).


The docs say that keywords find users by name and username. But they also find things you see on the profile under the image – bio, site, X, and company. Locations and languages will not be found.

Keywords support “normal” Boolean logic operators – AND, OR, NOT.

Partial Keywords

Partial words will be found (I haven’t seen this explained.) Example:

googl language:C


The most useful for sourcing are the operators language: and location:.

  • language: takes standard values
  • location: can be any text.

The note above in the “Keywords” “chapter” means that, when searching by location, you may also want to look for it as a keyword separately – like chicago -location:chicago.

If you search for a language that is not on their list, GitHub will ignore it; language:lisp language:nonexistent is the same as language:lisp (make sure you do not get caught here.)

The operator location: does work with accented characters; it is important for global Recruiters. Example: location:Київ.

Special characters under operators for location and language serve as a divider – and give you a Boolean AND –  location:NYC*SF. They are ignored at the beginning and end of the parameter.

Partial operator arguments will not be found.

Knowing that any user can only be found by one “main” language is essential.

With the operators, the Boolean OR is the default. (Incorrect in the docs.) NOT, for a change, is written as minus.

The other two operators search for the numbers of:

  • followers:
  • repos: (repositories).

The number format accommodates for “numrange” like 2..5, and these two – >, <. Example: followers:>1000 repos:>6 language:C. You can also write followers:=<3 or followers:<=3 (but not followers:=3)

A drop-down on GitHub search allows you to sort results by “best match,” followers, repositories, and join date. (Interestingly, GitHub user search API takes extra operators followers:, repositories:, and joined:, as well as sort: to run the same functionality).


Phrases in keywords should be in quotation marks. (Nice that they didn’t do anything unusual here 😉 ) Example: “NYC SF”.

However, spaces between quoted words under location: work as a Boolean AND! Example: location:”francisco san.” I haven’t seen this documented.


Parentheses are ignored. OR is executed first, then AND (same as on Google). I haven’t seen this documented.

The Good News

The good news is that, in practice, language: and location: “cover a large territory” and are often sufficient for collecting a sizeable promising user list to investigate further. Keep the search syntax subtleties covered above in mind.

Please join us for a deep dive into Github sourcing –

Leveraging GitHub: Advanced Data Mining and User Profiling

on August 30th, 2023, Wednesday.

Github Syntax and the LUSOG Tool Release

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Hello IT Recruiters:

We have fixed, improved, and documented the tool I recently announced at Sourcing Summit Tech. Access it here:

LUSOG = Let Us Search On Github.

Ensure you follow the installation and execution instructions strictly, starting with creating a copy of the LUSOG Table. There’s Help and a silent video with guidance. We hope it is straightforward.

The tool is free and provided “as-is,” offering Technical Recruiters massive power in searching for Software Developers. (We are in parallel developing a web-based tool on our future site Brain Gain Soft; please stay tuned.)

LUSOG finds GitHub users based on your search parameters, collects complete user data, including emails, and presents this information in a Google Sheet. 

In the backend, LUSOG runs GitHub’s REST API to search GitHub. The user is responsible for providing the Tool with their (free) personal GitHub API token. To use LUSOG, you must also give AppsScript permission to run and access the GitHub API. (This means you need to have GitHub and Google accounts.)

Select “Search GitHub Users” and enter search parameters according to GitHub’s syntax (see search help and user search help).

By default, LUSOG returns 100 results per query. You can request that the following 100 results be loaded by pressing the “Load More” button at the bottom. The maximum number of results for the same query is 1,000.

Imagine obtaining and exporting information like below in a few minutes while sipping your coffee!

I would be glad to hear how the tool serves you; please email me.

Now, if you are searching for Github users, with LUSOG or not, I must warn you that:

  1. Github Boolean search syntax is a monster, 
  2. Github search documentation contains mistakes in the Boolean section.

Github User Search Syntax

Your typical search for Developers would include the operator language: and several location: operators – or “qualifiers,” as Github calls them.

NOTE: if you search by a keyword, you will find the user’s name, nickname, bio, site, X (Twitter), and company – all the things on the profile page – but not the location or repository languages.

Github User Search supports a few more qualifiers in addition to language: and location:. Search terms can be arranged in a Boolean search statement. The syntax is bizarre (and mis-documented on the site):

Please join me at a new class

Leveraging GitHub: Advanced Data Mining and User Profiling

on August 30th, 2023. We will dive deep into GitHub sourcing and cover less-known approaches. Materials and support are provided. Seating is limited.

I hope to see you there!