Blogs

Why Hiring Fails Before It Even Starts

Define First, Hire Better: The 5-Step Candidate Targeting Framework

Sell Your Team Right: A Practical Framework for Hiring the Best

How to Start the First Conversation

Direct Sourcing Playbook for Talent Teams

A Strategic Guide to Hiring and Expansion in Southeast Asia

HR Strategy

Why Spotify Got Rid of Performance Scores?

HR Strategy

10 Employment Types Every Global Hiring Team Should Know

Recruiting Management

Both Sourcers and Recruiters play essential roles in helping companies find great talent. While they share a common goal—filling positions with the best candidates—their approaches, tools, skill sets, and candidate interactions differ significantly.

And understanding those differences? That’s become one of the most important keys to hiring success.

Sourcer vs. Recruiter: Why Role Clarity Matters

HR Strategy

Introduction

As artificial intelligence (AI) continues to reshape how we work, build products, and make decisions, organizations are looking for ways to tailor general-purpose language models to domain-specific use cases. In a previous article, we examined Instruction Tuning, a flexible method that allows models to follow natural language instructions without requiring massive retraining.

In this article, we explore two powerful yet fundamentally different approaches to customizing AI systems: Fine-Tuning and Retrieval-Augmented Generation (RAG). While both are used to specialize large language models (LLMs), they operate on different principles. Understanding their trade-offs is critical for building effective AI solutions in domains such as HR Tech, legal, and healthcare.

What is Fine-Tuning?

Fine-tuning is a method of adapting a pre-trained language model to a specific task or domain by continuing its training on labeled datasets. The model’s internal parameters (weights) are updated so that it performs better on the new task. For example, in an HR use case, a model can be fine-tuned on thousands of anonymized resumes to improve candidate-job matching accuracy. The diagram below illustrates this process: starting with domain-specific data, the information is structured and used to further train the pre-trained model. Through this additional training, the model’s weights are adjusted, enabling it to specialize in the new domain and deliver more accurate, task-specific results. This cycle can be repeated as more data becomes available, allowing the model to continuously improve and adapt to evolving requirements.

Open AI, Fine-Tuning (Image Reference-https://platform.openai.com/docs/guides/fine-tuning)

How Fine-Tuning Works

Data Preparation: Domain-specific or task-specific labeled datasets are prepared.

Which is Better for your AI? Fine-Tuning vs Retrieval-Augmented Generation

HR Tech

Hiring tech talent has become more difficult than ever. While demand is skyrocketing, finding truly capable developers is getting harder by the day.

In this environment, it’s natural for recruiters to start exploring sourcing channels beyond traditional job platforms. And one of the most promising alternatives is GitHub.

This guide explores why GitHub is emerging as a core sourcing channel for engineering talent, and how recruiters can effectively identify and engage with candidates using practical strategies.

Why Hiring Tech Talent Is So Difficult

Everyone agrees that hiring developers is tough—but the reasons why are more complex than just “a lack of candidates.” In reality, the challenge comes from a confluence of structural issues in today’s tech hiring environment.

1. A Supply-Demand Mismatch in Specialized Tech Roles

The number of developers globally continues to grow. But demand is highly concentrated in very specific areas.

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Among frontend engineers, companies want "React + TypeScript + Tailwind" experience

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In AI, they’re looking for "LLM fine-tuning experts" or "ML Ops practitioners"

A Practical Guide to Sourcing Engineers on GitHub

Talent Sourcing

Hello, this is Woosik Na, CEO of TalentSeeker.

At TalentSeeker, we deliver monthly product updates grounded in real customer feedback, always with one goal in mind:

to make hiring easier, smarter, and more efficient from start to finish.

This month’s update focuses on one essential question:

“How can we make talent search even easier?”

What if you could start hiring with just one line?

Until now, TalentSeeker has helped recruiters find top candidates quickly through structured filters and JD-based analysis.

But we asked ourselves:

[Update Story] Talent Sourcing in One Line

Update Story

The Evolving Role of HR in a Changing Business Landscape

The business environment in 2025 is evolving faster than ever. While the rapid adoption of artificial intelligence (AI) and automation has prompted many organizations to prioritize productivity, relying on technology alone is not enough to drive true performance. A human-centered approach is now recognized as the key to long-term success.

In fact, 53% of companies expect AI to improve productivity by 10–30%. But to realize these gains, it’s essential to support employees as they adapt to new ways of working.

"Technology is just a tool. It's the people who make a difference." – Steve Jobs

Why Human-Centered Work Redesign Is Necessary

Many companies expect greater efficiency from AI and automation, but these technologies alone won’t deliver results unless they are meaningfully integrated into employees' daily work. According to a recent HR report, 67% of companies do not change how people work when introducing new technology, which can significantly limit the ROI of digital investments.

Currently, 46% of HR leaders are planning to redesign work to support productivity gains. This strategy goes hand-in-hand with learning programs that help employees adapt to new tools and workflows, creating a positive synergy between technology and people.

Flexible work arrangements also play a critical role in human-centered redesign. According to Mercer, 64% of employees report being more productive when working remotely, showing that flexibility is just as important as technology in enhancing productivity.

HR leaders must help employees integrate AI naturally into their daily work. This includes creating AI-powered workflows and training programs that build confidence and competence in using new technologies.

Balancing Technology and People

Recruiting Management

The greatest danger in times of turbulence is not the turbulence itself, but to act with yesterday's logic. - Peter Drucker 

HR leaders must take on the role of true "risk managers" amidst economic uncertainty, rapidly changing market environments, and technological advancements like artificial intelligence (AI). According to an HR trends report, 64% of executives believe their organizations can withstand unforeseen shocks, but this is only achievable when risk management is integrated with people management.

Psychological Safety Determines Organizational Sustainability

"Psychological safety" refers to an environment where employees feel free to express their opinions without fear of making mistakes. It’s similar to how drivers feel more secure simply by knowing they are wearing a seatbelt on a slippery road.

According to an HR report, 57% of employees are working longer hours than before, with many experiencing high levels of stress. HR leaders must improve corporate culture to ensure employees feel psychologically safe in such circumstances.

Simply offering benefits is not enough. Organizations must create an environment where employees can voice their opinions, work autonomously, and embrace mistakes without fear. For example, Google’s Project Aristotle found that teams with high psychological safety outperform others in both innovation and productivity.

Risk Management Through a Healthy Corporate Culture

A healthy corporate culture is not just about having a "good atmosphere." It means creating an environment where employees can maintain their mental and physical well-being.

According to Mercer, 82% of employees feel at risk of burnout, which not only reduces productivity but also increases organizational risks.

Risk Management Through Building a Healthy Corporate Culture

Recruiting Management

New Challenges and Opportunities for Global Business

In today’s world, expanding into the global market is a critical strategy for gaining a competitive advantage. Especially for small and medium-sized enterprises (SMEs), limited resources and experience present many challenges when building and managing a global team.

Companies preparing for global expansion need to forecast future business needs and build resilient teams using flexible solutions. This article presents scalable business strategies for successfully managing global teams, from talent acquisition to crisis management.

1. Building a Scalable Talent Acquisition Strategy

Scalable workforce planning refers to the ability to flexibly expand talent acquisition and management systems to match the changing business environment. This involves not just hiring new employees but also addressing cultural fit, language proficiency, and utilizing flexible contract types based on project requirements.

Key Talent Acquisition Strategies

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Leverage Global Recruitment Channels: Actively utilize not only LinkedIn but also localized job platforms.

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Assess and Verify Fit: Implement structured interviews, skills assessments, and AI-driven assessment automation.

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Sustainable Talent Management: Offer reskilling and upskilling programs to respond to rapid growth.

How to Successfully Build a Global Team

Recruiting Management

Introduction

With the advancement of artificial intelligence (AI) technology, there has been a growing interest in building models optimized for specific tasks. In a previous article, we explored how to apply pre-trained models to specific domains using Transfer Learning. In this article, we will focus on a technique called Instruction Tuning, which fine-tunes AI models to respond more effectively to given instructions. This is a different approach from traditional Fine-Tuning, and it plays a critical role in enhancing the adaptability and efficiency of AI systems.

What is Instruction Tuning?

Instruction Tuning is a technique that enables AI models to understand and perform natural language instructions. While Fine-Tuning adjusts a model by using large amounts of data for a specific task, Instruction Tuning involves providing natural language instructions and corresponding example data to help the model perform tasks more flexibly and intuitively.

This method was proposed in the FLAN (Finetuned Language Models Are Zero-Shot Learners) paper, and its main goal is to improve the generalization performance of the model by providing explicit instructions for various tasks. For example, instead of just completing a sentence, a model might learn to understand and execute an instruction like "Summarize this sentence in one line."

How Instruction Tuning Works

Instruction Tuning is applied to large language models (LLMs) through the following steps:

Data Collection and Preprocessing: A dataset is created containing commands and corresponding input-output pairs for various tasks.

Model Training: The Instruction Tuning dataset is added to a pre-trained language model (e.g., T5, GPT, BERT) for further training.

Evaluation and Generalization Testing: The model’s zero-shot and few-shot performance is tested on various tasks to assess its generalization ability.

Instruction Tuning: flexible and need for less data

HR Tech

The First 44 Days: How Company Culture Decides Everything

While hiring top talent is crucial, what matters even more is ensuring these individuals stay with the organization. New employees typically assess whether the company is a good fit for them within their first 44 days. If their experience during this time isn't satisfactory, they are likely to consider leaving.

So, what are the factors that determine whether an employee stays or leaves? The answer is a strong company culture.

Why Company Culture Matters

Today, employees don’t just choose companies based on salary and benefits. Most employees consider whether the company's values and vision align with their own goals and values.

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94% of employees say that feeling a sense of belonging at work is important (Source: American Psychological Association)

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Organizations with positive cultures have employee retention rates that are 30% higher (Source: Gallup)

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Companies with a healthy culture have employee engagement levels that are 3.8 times higher (Source: Forbes)

In other words, the stronger the company culture, the more employees feel a sense of belonging, and the greater their long-term commitment to the company. One of the most important factors in building a strong company culture is employee recognition.

Employee Recognition Builds a Strong Company Culture

What is Employee Recognition?

Powerful Strategies to Increase Employee Retention

communication strategy

Hello, I'm woosik Na, the CEO of TalentSeeker.

At TalentSeeker, we are continuously updating features to improve the inefficiencies in the hiring process based on feedback from recruiters.

In our previous update, we introduced AI-powered talent search and communication optimization features under the theme “Faster and More Accurate with Large Language AI.”

In this update, we present the new ‘Communication Channel Integration’ feature to help recruiters overcome the hassle of switching between multiple platforms.

By integrating email and LinkedIn, we allow recruiters to handle all communications in one place and communicate with candidates in real-time.

The Inefficiency of Traditional Hiring Communication

Recruiters often need to use multiple communication channels during the hiring process. However, these platforms are often disconnected, making communication cumbersome and difficult to manage.

These are common concerns shared by recruiters in both startups and large corporations.

All Your Outreach at One Place

Update Story

HR: From Simple Management to Strategic Business Partner

As an HR professional, how strategically are you working?

In the past, HR was seen as a support function focused mainly on recruitment and personnel management. However, HR has now become a core growth driver for organizations. With increasing economic uncertainty and a greater focus on leveraging internal talent and improving performance rather than just recruitment, the role of HR has evolved from mere operational management to becoming a Strategic Business Partner.

What if hiring slows down? How can existing talent be developed?

What if performance stagnates? What are the practical ways to improve employee performance?

What if data is leaked? How can confidentiality and security issues be managed?

Now, HR is responsible for both performance management and data protection, driving the organization’s ongoing growth and innovation. So, how can HR play this strategic role effectively?

This article will explore how HR can enhance its strategic role in organizations by focusing on Performance Management and Data Confidentiality.

Performance Management: How HR Drives Organizational Growth

Why is Performance Management Important?

The Evolving Role of HR: From Performance Management to Data Protection

HR Strategy

Why Salary Negotiations and Onboarding Matter in Employee Retention Strategies

Employee retention is not just about offering a competitive salary or increasing benefits. Fair salary negotiations from the start and a structured onboarding process are key elements that determine long-term employee satisfaction and retention.

According to recent research from BambooHR, 50% of employees are struggling to make ends meet, and 73% are asking for a salary increase in their current roles. Furthermore, 70% of new hires decide whether the organization is a good fit within the first month, and 44% regret their decision within the first week.

These statistics make it clear how salary negotiations and onboarding play a crucial role in employee retention. When employees feel that they are receiving a fair salary and are treated equitably, and when they successfully adapt to the company culture in the early stages of their employment, the likelihood of long-term retention increases.

In this article, we'll provide 5 salary negotiation tips and 5 onboarding goals that HR professionals can implement to improve employee retention.

Salary Negotiation: The First Step in Employee Retention

Salary negotiation is not just about setting a salary. It’s an important moment to communicate how valued the employee is within the organization. If salary negotiations are not handled properly, employees may lose trust in the organization and be more likely to seek better opportunities.

1. Total Compensation Package

Salary negotiation isn’t just about the base salary. Employees consider the total compensation package, and HR professionals must effectively communicate this to them. Before starting salary negotiations, HR professionals should understand the total compensation package the organization can offer and be ready to explain it.

Compensation Elements to Consider:

Flexible Work Arrangements: Offer remote work or hybrid work options.

How to Get the First Step Right After Hiring: A Guide to Salary Negotiation and Onboarding

HR Strategy

Hello, this is woosik NA, the CEO of TalentSeeker.

At TalentSeeker, we continuously update our features based on customer feedback. In the previous update, we introduced AI-powered talent discovery and communication optimization tools under the theme "Faster and More Accurate with Large Language AI."

In this post, we are excited to introduce one of the key features from the previous update: Personalized Candidate Email Sequences. We will explore how this feature differs from traditional recruitment methods and how it can revolutionize communication in real-world hiring processes.

Limitations of Traditional Recruitment Emails

Through conversations with countless recruiters, we’ve heard about the difficulties they face in writing and managing recruitment emails.

"I need to write different emails for each candidate, but it takes all day."

"When candidates don’t respond, I’m unsure if I should follow up or just give up."

"I’ve emailed the same candidate before, but can I send another one?"

These challenges are common across all industries and company sizes. Recruitment email inefficiency is a pain point for all recruiters, whether they work at startups, IT companies, or large corporations.

Writing Custom Messages for Each Candidate Takes a Lot of Time

Automated. Personalized. Effective Emails

Update Story

Hello, this is woosik NA, CEO of TalentSeeker.

At TalentSeeker, we continue to make regular updates every month based on feedback from our customers. In our last update, we introduced AI-driven features to help hiring managers discover candidates faster and communicate more efficiently under the theme “Faster and More Accurate with Large Language AI.”

In this article, we will dive into one of the major features from the last update, Conversational Talent Search (TalentGPT), and explore how this technology is different from traditional hiring methods and what changes it can bring to the hiring process.

Limitations of Traditional Talent Search Methods

The recruitment market is rapidly evolving, yet traditional talent search methods still face many limitations. After interviewing numerous hiring managers, we identified the key issues with existing talent search methods.

Challenges such as inefficient search processes, superficial keyword matching, difficulty in fine-tuned searches, and the lack of incorporation of the latest technological trends have led hiring managers to spend significant time and effort to find the best candidates.

Complex Filters: Inefficient Search Process

In the traditional search method, hiring managers typically combine multiple filters to find the desired candidates. However, this manual search process has several limitations:

Fatigue from Repetitive Filter Adjustments

Just Ask. TalentGPT Delivers

Update Story

Introduction

Large Language Models (LLMs) are driving innovative changes in the field of Natural Language Processing (NLP). Models like GPT-4, BERT, and T5, with billions of parameters, learn language structures and meanings from vast datasets, enabling them to understand context like humans and respond to complex queries.

The success of these models is based on two pillars: pre-training and transfer learning. However, training an LLM from scratch for every task is impractical due to the massive amounts of data, computational resources, and time required. This is where Transfer Learning becomes crucial, as it allows leveraging knowledge from a pre-trained model and applying it to new domains or tasks, greatly enhancing efficiency.

This article explores the core principles and theoretical background of Transfer Learning and how it can be applied in LLMs.

The Concept and Approach of Transfer Learning

Transfer Learning is a learning technique where knowledge acquired from one task is transferred to another. It is particularly useful in large language models. Typically, Transfer Learning involves two stages: pre-training and fine-tuning.

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Pre-training: In this phase, large language models are trained on vast, generic data to perform language modeling. They learn the general patterns of various languages, grammar, common-sense knowledge, and semantic relationships. For example, models like BERT and GPT learn to understand the relationships between words in sentences by analyzing large amounts of text data.

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Fine-tuning: After pre-training, the model is further trained on specific data related to a particular task or domain. In this phase, fine-tuning optimizes the model’s performance for specialized tasks, like predicting medical diseases or analyzing documents.

This approach greatly enhances data efficiency and time efficiency. By using pre-trained models, the time and resources required for training are significantly reduced compared to training a model from scratch.

Transfer Learning: How to Leverage LLMs More Efficiently

HR Tech