As AI continues to permeate the recruitment industry, it’s clear that the right tools can streamline workflows, reduce costs, and improve hiring decisions. But with an increasing number of AI tools entering the market, figuring out which one is the best fit for your team can be a challenging task.
The good news? You don’t need to be an AI expert to make smart decisions. By understanding just a handful of key AI terms, you’ll be able to cut through the noise and focus on what really matters—choosing tools that align with your goals and deliver real, measurable results for your recruitment business.
That’s why we’ve put together this no-fuss glossary of 30 essential AI terms—each explained in clear, straightforward language. Our goal is simple: to help you feel confident in making the best choices for your team, without getting bogged down by technical jargon or unnecessary complexity.
Key AI Concepts for Recruitment
Core AI Concepts | AI in Recruitment |
AI Models and Systems | |
Challenges and Ethics in AI | |
Key AI Terms for Functionality | |
Data and Processing in AI | |
Core AI Concepts
1. Artificial Intelligence (AI)
AI refers to technology that allows machines to perform specific tasks that typically require human intelligence, such as learning, problem-solving, understanding language, recognising images and making decisions.
Rather than thinking like we do, artificial intelligence works by processing large of amounts of data. Think of it as training a very sophisticated digital assistant that can recognise pictures, follow instructions, make predictions or even figure out the best candidate match for a job. While today's AI is very good at specific tasks, it's not actually "intelligent" in the way humans are – it's more like a very advanced tool that can spot patterns and follow complex instructions.
2. Artificial General Intelligence (AGI)
AGI is a theoretical future AI system that would match or surpass human cognitive capabilities across a wide range of cognitive tasks.
While current AI tools are specialised for specific tasks (like screening CVs or matching candidates to jobs), AGI would be versatile with human-like abilities to reason, learn, and adapt across any situation without special training. In recruitment, it would be the equivalent to having an AI system that could not only screen CVs, but also write job descriptions, conduct video interviews, negotiate salaries, onboard new hires, and develop hiring strategies – all with human-level understanding and flexibility. While major tech companies like Google and OpenAI are working towards developing AGI, it remains a future goal rather than a current reality, with no clear timeline for when it might be achieved.
3. Machine Learning
ML is a subset of AI where computers automatically learn from data and improve without being explicitly programmed.
In recruitment, ML is commonly used to scan through thousands of CVs to find suitable candidates, predict which candidates are most likely to succeed in a role based on past hiring data, or even flag potential biases in job descriptions.
4. Deep Learning
Deep Learning is an advanced type of Machine Learning that uses neural networks, similar to the human brain, to analyse information in layers, recognise complex patterns in data and make decisions.
In recruitment for example, Deep Learning enables AI tools to better understand the context in CVs and job applications. For example, it can recognise that "managed a team of 5" and "led 5 direct reports" mean the same thing, making candidate screening more intelligent than simple keyword matching.
AI Models and Systems
5. Large Language Models (LLMs)
LLMs are AI models trained on enormous amounts of text data to understand, generate and communicate in human-like language.
They power tools that help recruiters write personalised emails, job ads or other marketing content in seconds, providing human-quality content at scale. ChatGPT is perhaps one of the most recognised and widely used LLMs today.
6. Fine-tuned Model
A Fine-tuned Model is an existing AI model that has been pre-trained on specific industry data to perform tasks more accurately for that particular field.
For example, AdScribe has been trained specifically on high-quality recruitment and marketing data to make it more effective at recruitment-specific tasks such as generating compelling job adverts and candidate profiles, compared to general AI-models.
7. Generative AI
Generative AI is a broad type of artificial intelligence that can create new content including text, images, videos, and code based on the data it has been trained on.
Generative AI tools can assist with creating various types of content - from writing job descriptions to designing entire recruitment hiring campaigns. For example, while Large Language Models (a type of Generative AI) can write job descriptions and candidate emails, other Generative AI tools can create images for blog posts or generate marketing videos. However, while these tools are powerful assistants, they still require human review and refinement to ensure accuracy and appropriateness.
8. Multi-Model AI
Multi-Model AI refers to platforms that integrate multiple AI models, selecting and applying the most appropriate one based on specific tasks or requirements.
Unlike platforms that rely on a single AI model for all tasks, Multi-Model AI systems leverage different AI models' unique strengths to ensure optimal results. For example, AdScribe integrates different Large Language Models like ChatGPT and Claude, automatically selecting the most suitable model based on the task - using one model for creative content, another for data analysis, and different models for long-form versus short-form content. This approach ensures the best possible output by matching each task with the AI model best suited to handle it.
Key AI Terms for Functionality
9. Parameters
Parameters are the internal settings and rules that AI models use to make decisions.
These settings are adjusted during training to improve the AI’s ability to solve problems or generate accurate responses. For example, a parameter might be the level of creativity an AI-machine should follow when generating outputs. The higher the temperature of creativity, the more imaginative the response. The less the temperature of creativity, the more logical and factual the response.
10. Tokens
Tokens are the basic units of text (like words or parts of words) that AI systems process and analyse to understand and generate language.
In recruitment, understanding tokens is important when using AI tools like ChatGPT as AI systems often have limits on how many tokens they can process at once.
11. Prompts
Prompts are specific instructions or questions given to AI systems to guide them in generating relevant responses.
Effective prompts are crucial for getting the best results from AI tools. For example, instead of asking "Write a job description," a better prompt would be "Write a job description for a senior sales manager with 5 years experience in SaaS sales.” AI will follow this instruction to generate relevant content.
12. Context
Context refers to the background information or previous interactions that help AI systems understand and respond more accurately.
Providing context helps AI generate more relevant outputs. For example, telling an AI system about your company culture and values before asking it to write a job description will help it create more appropriate and aligned content. Context might also include your preferred tone of voice, who your target audience is and other information that will provide enough data for the AI model to produce more accurate and authentic content. Similar to prompts, the quality of prompt and context are important to generate better content.
13. Datasets
Datasets are collections of information, such as job descriptions or CVs, used to train AI models to recognise patterns and make predictions.
The quality of an AI tool's training dataset is crucial. For example, an AI system trained on a dataset of successful hires and their characteristics can better identify promising candidates in future recruitment drives.
14. Algorithms
Algorithms are the set of step-by-step rules and instructions that AI follows to process data and complete tasks.
Algorithms power various AI functions, such as matching candidates to jobs based on specific criteria. However, it's important to regularly review these algorithms to ensure they don't perpetuate biases.
AI in Recruitment
15. AI Content Creation
AI Content Creation refers to using AI tools to automatically generate various types of content.
AI can help create job descriptions, candidate outreach emails, social media posts and other recruitment marketing materials in seconds. While AI can generate high-quality first drafts, human review is essential to ensure the content aligns with brand voice and accuracy.
16. AI Automation
AI Automation is the use of AI to handle repetitive, time-consuming tasks without human intervention.
This includes automating CV screening, scheduling interviews, sending follow-up emails, and updating candidate records. For example, AI can automatically screen thousands of CVs against job requirements, saving recruiters hours of manual work.
17. AI Augmentation
AI Augmentation combines AI capabilities with human expertise to enhance performance and decision-making.
Rather than replacing, for example, recruiters, AI augments their abilities by providing data-driven insights and recommendations. For example, while AI might shortlist candidates based on matching criteria, recruiters make the final hiring decisions using their experience and judgment.
18. Predictive Analytics
Predictive Analytics uses AI to analyse historical data to forecast future outcomes and make data-driven decisions.
By analysing past successful hires for example, these tools can predict which candidates are likely to succeed in specific roles, potential retention rates, or even future hiring needs. For example, the system might identify that candidates with certain skills or experiences tend to perform better in particular roles.
19. Conversational AI
Conversational AI refers to AI systems that can engage in human-like dialogue to interact with others.
These include chatbots and virtual assistants that can answer candidate questions, schedule interviews, or collect initial application information. This technology helps provide 24/7 support while reducing administrative work.
20. AI Agents
AI Agents are future autonomous systems that would be able to independently perform specific recruitment tasks without constant human oversight.
These agents are envisioned to manage routine tasks like sending interview invitations, updating candidate status in ATS systems, or coordinating with hiring managers. While they would work independently, they would still operate within defined parameters and require human supervision. Like AGI, this technology remains theoretical and is not yet available in current recruitment tools.
Challenges and Ethics in AI
21. Hallucination
Hallucination occurs when AI systems generate incorrect, irrelevant or completely fabricated information despite appearing confident in their response.
This might happen when an AI tool for example, invents candidate qualifications or creates false job requirements that don't exist. For this reason, all AI-generated content needs human verification before being used in recruitment processes.
22. Bias
Bias in AI refers to unfair or discriminatory patterns in AI decision-making, often reflecting historical biases present in the training data.
For example, if an AI system is trained primarily on historical hiring data where certain groups were underrepresented, it might unfairly favour or discriminate against certain candidates. Regular monitoring and diverse training data are essential to prevent perpetuating biases in recruitment.
23. AI Ethics
AI Ethics encompasses the principles and practices that ensure AI is used responsibly and fairly in recruitment processes.
This includes ensuring transparency about AI use, maintaining human oversight of AI decisions, and protecting customer privacy.
24. AI GDPR
AI GDPR refers to how AI systems must comply with data protection laws (GDPR) when processing personal data.
Compliance ensures that AI technologies handle personal data in a lawful, transparent, and ethical manner. This includes implementing proper data handling and secure storage procedures to prevent data breaches or misuse. However, as with all technology, there is never a 100% guarantee of security, so best practice is to avoid including sensitive personal data unless absolutely necessary.
Data and Processing in AI
25. Big Data
Big Data refers to extremely large sets of data that can be analysed by AI systems to reveal patterns, trends, and insights.
This includes processing vast amounts of structured and unstructured data from various sources. For example, in recruitment, this could involve analysing thousands of applications and hiring outcomes to identify successful candidate patterns.
26. Data Mining
Data Mining is the process of examining large databases to discover patterns and generate new insights from the data.
This involves finding meaningful connections and patterns that might not be immediately obvious. The insights gained can then be used to make better-informed decisions and predictions.
27. AI-Native
AI-Native refers to products and companies built from the ground up with artificial intelligence at the core of their operations and services, often designed and optimised for a specific industry.
Unlike traditional companies that add AI capabilities to existing systems (such as an established CRM provider adding on AI features), AI-Native companies are architected around AI from their foundations. This allows them to fully exploit AI capabilities in ways that retrofitted systems cannot, enhancing innovation and efficiency in their domain. For example, AdScribe, an AI-native tool built specifically for recruitment, has designed its architecture around AI which means, instead of generating one piece of content at a time, it can generate a full recruitment campaign complete with job advert, social posts, outreach email, interview questions and more, in a single click.
28. AI Parsing
Parsing is the AI technique that automatically extracts and organises key information from documents into structured, usable data.
This technology breaks down documents and text to identify and categorise specific pieces of information. In recruitment, for example, parsing technology can automatically extract skills and experience from CVs.
29. AI Scraping
Scraping involves using AI to automatically collect and analyse information from public online sources.
This technique enables the collection of large volumes of information by simulating human browsing behaviour. For example, you might use scraping to gather information about your client’s company to provide better context for AI to generate a more compelling and grounded job description. While AI scraping can enhance data analysis and insights for various applications, it must be conducted in compliance with legal and ethical standards, including respect for copyright and privacy regulations.
30. Prompt Engineering
Prompt Engineering is a specialised skill of crafting precise instructions for AI models to generate optimal outputs and achieve desired results.
This involves understanding how AI models interpret instructions and knowing how to structure prompts with the right context, parameters, and specific requirements. For example, an expert prompt engineer would know that using certain language, following specific structure, including formatting instructions, tone requirements, and specific examples in their prompts helps AI generate more accurate and consistent responses. It's becoming an increasingly important skill as organisations seek to get the best results from AI tools.
Final Remarks
Understanding the nuances of AI can feel overwhelming—especially with so many buzzwords and technical information to sift through. But understanding the basics doesn’t have to be complicated, and it can be the competitive-advantage your agency needs to make the best decisions when it comes to adopting new AI technology.
By getting familiar with these key AI terms, you’ll gain the confidence to have better conversations with AI providers and to choose solutions that truly benefit your team. Imagine being able to automate those tedious tasks that eat up hours of time, improve candidate and client relationships, and speed up time-to-fill—without having to become an AI expert yourself.
AI is here to simplify your life, not complicate it. With the right knowledge, you can find tools that deliver real results, freeing you to focus on what matters most: building successful teams and growing your business.
Now that you’re familiar with these essential terms, you’re one step closer to making better AI decisions for your business. And if you're curious to see how AI can truly benefit your team, let us show you in action. Click here to book a personalised demo with AdScribe and see practical AI solutions first-hand, exclusively designed for the recruitment industry.
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