What Is A Large Language Model (LLM) In AI

Last updated on 26 Apr 2025

What are large language models, how they work, best practices in using the technology.

Large Language Models (LLMs): An Overview

Large Language Models are AI systems that understand and generate human-like text. They use deep learning on enormous text datasets to learn language patterns [1]. LLMs are a form of generative AI – they can produce original text (stories, answers, summaries, code, etc.) rather than just classifying input. These models are very large: typical LLMs have billions of parameters (learnable weights) [1]. For example, OpenAI’s GPT-3 model has 175 billion parameters [2], enabling it to write fluent paragraphs, answer questions, translate languages, and more.

How LLMs Work

Transformer architecture: Modern LLMs are built on the Transformer neural network. The input text is first broken into tokens (words or subwords) and mapped to vector embeddings [3]. The transformer uses self-attention layers so that each token can “attend” to all others in the sequence, capturing context and relationships [3][4]. Feed-forward layers then process these contextual representations. This architecture (multi-head attention + feedforward) lets the model learn complex language patterns more efficiently than older RNN/CNN models [3][4].
Pretraining on massive text: LLMs undergo pretraining on huge text corpora (e.g. web text, books, code repositories). During pretraining, the model learns language structure by tasks like predicting the next word or filling in blanks [5]. In effect, it ingests trillions of words without explicit supervision, gradually learning grammar, facts, and word meanings from the data [5].
Fine-tuning and prompting: After pretraining, an LLM can be fine-tuned on a specific task or domain to improve performance (for example, tailoring it to legal documents or customer support). Another common method is prompting: the user gives the model instructions or examples. In few-shot prompting, you provide sample Q&A or input-output pairs so the model infers the task [5]; in zero-shot, you phrase the task as an instruction (e.g. “Summarize the following text.”). These techniques steer the pretrained model without retraining its weights.

Popular LLM Examples

OpenAI GPT-3 (2020): A flagship LLM with 175 billion parameters [2]. GPT-3 demonstrated strong performance on translation, Q&A, writing, coding, and more, by predicting text one token at a time.
OpenAI GPT-4 (2023): The successor to GPT-3. GPT-4 is even larger (exact size unrevealed) and is multimodal (it can accept images and text as input). It achieves near-human performance on many benchmarks (e.g. scoring in the top 10% on the bar exam) [6], thanks to more training and better alignment.
Anthropic Claude: A family of chat-focused LLMs by Anthropic. Claude models are optimized for safe dialogue and reasoning. For example, Claude 2 (2023) can process an unusually large context (about 100,000 tokens, roughly 75,000 words) in one prompt [7], letting it handle very long documents or conversations.
Meta LLaMA (Large Language Model Meta AI): Released in 2023 for research use, LLaMA models come in 7–65 billion parameter sizes [8]. LLaMA-13B (13 B parameters) outperforms GPT-3 (175 B) on many benchmarks, and LLaMA-65B rivals the best models of its time [8]. Meta later open-sourced LLaMA 2 (models of 7B, 13B, 70B) for research and commercial use.
Other examples: Google’s Bard/Gemini (based on PaLM and other Google models), Microsoft’s Bing Chat (using GPT-4), Amazon’s AlexaTM models, and a growing number of community models (e.g. Mistral, Falcon, etc.) are also prominent LLMs.

Best Practices and Limitations

Clear prompting: Write prompts as explicit instructions. Put the task description first and be specific about desired format, style, or length [9]. Providing examples or a template in the prompt (few-shot examples) often yields better results [9][10]. For instance, asking “Translate the following text into French:” and giving the text will usually work better than a vague query.
Fine-tune or specialize if needed: If you have domain-specific needs (legal, medical, coding, etc.), consider fine-tuning an open model on your data or using a model pretrained for that domain. Alternatively, carefully constructed prompts can often adapt a general LLM to many tasks without full retraining [5][5].
Fact-check (avoid hallucinations): LLMs can confidently produce false or made-up statements – a phenomenon known as hallucination [11]. For example, an AI might invent a citation or fabricate historical facts. These errors can have real consequences (e.g. there have been legal cases where an AI-generated summary blamed a real person for crimes they never committed) [11]. Always verify important outputs against reliable sources. When accuracy is critical, use retrieval-augmented methods (having the model cite real references) or constraint-based prompting.
Beware of biases: Because LLMs learn from human-created text, they can absorb social and cultural biases. Past AI tools have shown issues (e.g. a hiring tool biased against women) [12]. Review model outputs for fairness and avoid deploying unvetted responses in sensitive contexts. Using techniques like bias auditing, adding fairness instructions in prompts, or human oversight can help mitigate these issues.
Respect usage policies and ethics: Follow provider guidelines and laws. Do not use LLMs for illegal, harmful or unethical tasks. For example, OpenAI’s policies forbid using their models to break laws or produce abusive content [13]. Do not reveal or input personal/sensitive data – sharing confidential information with an LLM risks privacy breaches (OpenAI explicitly disallows compromising others’ privacy) [13]. Be transparent when content is AI-generated (avoid misleading others into thinking it’s human-written).
Understand model limits: Each LLM has a fixed training cutoff and context length. They generally won’t know about events after their last training date. Also, models have a maximum “context window” (the amount of text they can process at once). For example, Claude 2’s context is ~100k tokens [7], but many models cap out at a few thousand tokens. Designing prompts within these limits is crucial. If a task exceeds the context, consider splitting it or using iterative prompts.
Iterate and adjust: Treat interaction with LLMs as an iterative process. If the output isn’t right, tweak the prompt: add clarification, simplify the task, or break it into steps. Lowering the “temperature” parameter (if available) makes the model more deterministic, which can improve factuality. For creative tasks, higher temperature may add variety. Experiment and refine prompts based on the model’s responses.

In summary, LLMs are powerful tools for generating and understanding text, powered by transformer networks trained on vast data [5][3]. Popular examples include OpenAI’s GPT series, Anthropic’s Claude, and Meta’s LLaMA models [2][6][7]. To use them effectively, craft clear prompts, verify outputs, and follow ethical guidelines. By understanding their mechanics and limitations, users can leverage LLMs for creative and practical applications while avoiding pitfalls like hallucinations or bias [11][13].

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