We’ve reached the point where AI can respond to your questions with near-human clarity.
Condensing complicated legal documents into clear summaries, igniting fresh perspectives from data analysis, guiding you expertly through extensive codebases, and even helping you write that persuasive and professional email to your boss.
That's what LLMs are all about. Fueled by breakthroughs in deep learning and the availability of massive datasets, the field is experiencing explosive growth, with new models and applications emerging daily. Changing how we work, learn, and communicate.
LL-What-Now?
Large Language Models (LLMs) are AI systems trained on a wealth of text, from books and academic papers to websites and code repositories. It does more than just store data, though. These models learn the intricate patterns of language, including the subtle nuances of grammar, the precise meanings of words (semantics), and the contextual clues that shift meaning. This means that LLMs can create responses that are strikingly similar to those of a human across a wide range of tasks.
You can train their flexible “foundation models” to handle everything from support agents to deciphering enterprise-grade analytics. And while they’re not perfect (they can hallucinate facts or pick up biases from their training data, which we’ll discuss later), LLMs still represent one of the most significant leaps forward in AI helping bridge the gap between human and computer.
Where do they fit in the AI family tree?
In the AI family tree, machine learning (ML) forms the foundation for pattern learning. Deep Learning is a branch that mimics the structure of the human brain with neural networks. NLP (Natural Language Processing) specialises in understanding and generating human language. Then LLMs sprout from the NLP branch, empowered by deep learning and enormous volumes of training data:
How Do LLMs Work?
Input (Prompt):
You provide a request, like “Help me write a company wide email…”
Tokenise:
The prompt is divided into tokens, like words or bits of words, so the model can handle them one by one.
Transform Stack:
LLMs (like GPT) use Transformers, which excel at tracking relationships between tokens over long ranges, letting the model handle context across entire paragraphs.
Predict:
At each step, the model predicts which word (token) is most likely to come next, based on all the text it’s seen during training.
Respond:
The model assembles these predictions into a coherent output, word by word (or token by token).
What Can LLMs Be Used For?
Content Creation: Write blog posts, marketing copy, emails, or even short stories at scale.
Customer Service: Power chatbots and FAQs that handle routine questions before escalating complex issues to human agents.
Summaries & Research: Condense dense documents or academic papers into concise overviews, saving hours of reading.
Translation & Localisation: Adapt text across languages, smoothing out nuances and cultural references.
Creative Brainstorming: Writers, marketers, and content creators use LLMs to spark fresh angles or story ideas.
Coding Partner: Tools like GitHub Copilot rely on LLM technology to suggest code snippets, debug, and speed up software development.
The LLM Marketplace
From customisable, open-source models to gigantic, closed-source systems with billions of parameters, large language models come in all shapes and sizes. Here are a few LLMs available right now:
GPT-4 (OpenAI)
The model everyone knows for its advanced reasoning and ability to handle different tasks
Powers ChatGPT and various enterprise integrations
Closed-source, but accessible via API (priced per token)
GPT-Neo & GPT-J (EleutherAI)
Open-source alternatives inspired by the GPT architecture
Researchers and developers can download and fine-tune them for specialised tasks
LLaMA (Meta)
Meta offers open-source LLMs with different capacities for learning. Bigger models learn more complex stuff, but they also cost more to run.
Focuses on making large-scale language models more accessible to the research community
MPT (MosaicML)
A family of open-source Transformer models designed for efficient training
Used by businesses seeking more control and customisation
Claude (Anthropic)
A closed-source model designed with safety and transparency in mind
Emphasises AI “alignment,” meaning it aims to avoid harmful or biased outputs
Downsides of LLMs
It's not all sunshine and rainbows with LLMs; they have their drawbacks. Some of which may either be a deal breaker or change how you test, validate and launch your solution..
Hallucinations: LLMs can produce confident-sounding but factually incorrect info. Without a fact-check or retrieval mechanism, they might mislead users.
Bias & Ethical Concerns: Learning from internet-scale data means inheriting societal biases present in that data.
High Computational Costs: Training large models consumes vast compute resources, driving up costs and leaving a hefty environmental footprint.
Stale Knowledge: LLMs only know the data they were trained on. Without Retrieval Augmented Generation (RAG) or frequent updates, they won’t keep pace with breaking news or new data.
Security & Privacy Risks: Fine-tuning with proprietary data poses risks if the model inadvertently “leaks” sensitive details in responses.
Over-Dependence: Organisations might rely too heavily on LLMs for critical tasks, neglecting vital human oversight and expertise.
Build Your Own vs. Rent an LLM
DIY LLM or go with something like OpenAI? What's the plan? Every option has advantages and disadvantages.
Building Your Own
Pros:
Full Control: You can tune parameters, architecture, and training data for specific business needs.
Data Ownership: Proprietary data remains on your own servers, reducing risks of external data exposure.
Customisation: You can fine-tune the model in ways hosted services may not allow, tailoring it to niche use cases.
Cons:
High Cost & Complexity: Training large models requires significant computational resources, specialised expertise, and ongoing maintenance.
Longer Development Cycles: You’ll need dedicated data science and engineering teams to build and iterate on the model.
Limited Support & Updates: You’re on the hook for troubleshooting, bug fixes, and performance tuning.
Renting an LLM (Hosted Services)
Pros:
Fast Deployment: Spin up a solution quickly via an API, no need to train a giant model from scratch.
Lower Upfront Costs: Pay per request or monthly fees instead of massive hardware and electricity bills.
Ongoing Improvements: Providers like OpenAI continually update and improve their models, benefiting your application automatically.
Cons:
Less Customisation: You’re limited to parameters and capabilities the provider offers, which may not align perfectly with your needs.
Potential Data Exposure: Sensitive data may pass through third-party servers, raising privacy or compliance concerns.
Vendor Lock-in: Switching providers can be difficult if your workflow relies heavily on proprietary APIs.
Wrapping It Up
Large language models are not merely a passing trend. Across industries, LLMs are supercharging creativity and productivity. However, challenges remain; cost, bias, and inaccuracies require careful consideration and responsible implementation.
Building your own LLM offers unmatched control and customisation but comes with steep technical and financial overhead. Renting a hosted LLM is often faster and cheaper to get started, yet you sacrifice control and face potential data security concerns.
Get ready for LLMs to change how we research, make discoveries, and get things done. Helping change our workflows and help us and organisations find better ways to work.