AI is now a practical part of work. But “AI” gets used as a catch-all term, which some times creates confusion.
This post explains the difference between traditional AI and large language models (LLMs) in plain language, with examples, limitations, and how to choose the right approach. It is written for people who need clarity, not a computer science lesson.
What people usually mean by “traditional AI”
In most organisations, “traditional AI” refers to two things:
- Rules and logic: These systems follow explicit instructions. If X happens, do Y.
- Machine learning that predicts numbers or categories: These models learn patterns from data, then output things like a score, a label, or a forecast. Think fraud risk score, demand forecast, or churn probability.
Traditional AI is best when the task is clear, repeatable, and measurable.
Examples of traditional AI you see in real businesses
- Fraud detection and credit risk scoring
- Forecasting sales or stock needs
- Sorting emails into spam or not spam
- Detecting anomalies in finance, security, or operations
- Recommending products based on past behaviour
What traditional AI is good at
- Predictable outputs for defined tasks
- Strong performance when you have good quality structured data
- Easier testing in narrow use cases, because success is easier to measure
Where it struggles
- Tasks that need flexible language understanding
- Work that changes shape often, where you would need to keep rebuilding rules or retraining models
- Explaining decisions in human-friendly language without extra work
What an LLM is, and what makes it different
A large language model is a type of deep learning model trained on huge amounts of text. It can generate and work with language across many tasks, like answering questions, summarising, drafting, and classifying text.
LLMs are usually built using transformer-based architectures and trained to predict the next likely words in a sequence. In practice, this is why they can hold a conversation, write a first draft, and turn rough instructions into structured output.
Examples of LLMs use at work
- Drafting and rewriting emails, reports, and proposals
- Summarising long documents and pulling out key points
- Searching internal knowledge in plain English, if connected to company data
- Supporting customer service teams with suggested replies
- Helping developers write and review code
What makes LLMs powerful
- They can switch tasks without being rebuilt each time
- They can follow instructions in normal language
- They can work with messy text data that traditional tools struggle with
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The most important differences, in plain terms
1) Output
- Traditional AI often outputs a score, label, or forecast.
- LLMs output language. They can also structure information into tables, checklists, or steps, but the core output is text.
2) Flexibility
- Traditional AI tends to be excellent in a narrow lane.
- LLMs can cover many language tasks, but you must control where you rely on them.
3) How they fail
Traditional AI fails in fairly “obvious” ways. Bad data leads to bad predictions.
LLMs can fail in a more risky way. They can produce text that sounds confident but is wrong:
- LLMs can hallucinate: This is not a small edge case. It is a known behaviour.
- They do not have “live knowledge” by default: Unless connected to tools or up-to-date sources, an LLM will not know what happened this morning, what is in your CRM, or what is in your policy folder
- They can leak risk if you treat them like a database: If you want factual answers, you need grounding. One common approach is retrieval augmented generation, where the model is forced to work from trusted documents rather than “making a best guess”. Microsoft’s guidance focuses on retrieval, prompt design, and evaluation as key ways to reduce hallucinations in real systems.
- High-stakes use needs governance: If the output affects customers, money, legal positions, or health, you need controls. NIST’s Generative AI Profile is designed to help organisations identify and manage GenAI-specific risks.
Kalisa is built for this exact gap between “LLMs are useful” and “LLMs are safe enough for real work”. It helps teams ground responses in trusted, domain-specific knowledge, and reduce hallucinations with guardrails, so internal and client-facing AI outputs are more reliable. It also supports building secure AI-driven experiences and workflow automation, which is especially relevant in regulated environments.
How to choose the right approach
Use traditional AI when:
- You need high predictability
- The task is well-defined
- You can measure success clearly
- You mainly work with structured data
Use LLMs when:
- The job is language-heavy
- You need fast drafting, summarising, or Q&A
- The main value is speed and usability, not perfect precision
- You can put checks in place
Use a hybrid approach when:
- You need language flexibility but also factual accuracy
- You want an LLM front end that uses trusted internal sources
- You want the system to call specialised tools for maths, search, or workflow steps
Getting Started with End-to-End AI Transformation
Partner with Calls9, a leading Generative AI agency, through our AI Fast Lane programme, designed to identify where AI will give you a strategic advantage and help you rapidly build AI solutions in your organisation. As an AI specialist, we are here to facilitate the development of your AI strategy and solutions within your organisation, guiding you every step of the way:
- Audit your existing AI capabilities
- Create your Generative AI strategy
- Identify Generative AI use cases
- Build and deploy Generative AI solutions
- Testing and continuous improvement
