Understanding LLMs is the Secret to Marketing Content That Performs
As the digital landscape evolves, so do the tools we use to create engaging, effective content. One emergent tool that has gained significant traction is the Large Language Model (LLM). LLMs are artificial intelligence (AI) models trained on vast amounts of text data, enabling them to generate human-like text based on the input they receive. They are transforming the way we approach content creation, making it more efficient and innovative.
However, to leverage them effectively, it's crucial for content marketers to understand their basics. This includes understanding how they work, their strengths and limitations, and the ethical considerations involved in their use. We'll delve into these topics, providing you with the knowledge you need to effectively incorporate LLMs into your content marketing strategy.
We'll pull back the curtain on what LLMs are, how they work, and how they are trained. We’ll give you insights into the complex process that enables these models to generate intelligent, relevant text and cover the top five most popular LLMs that are not only pushing the boundaries of AI capabilities but also revolutionizing content marketing strategies.
While you can certainly leverage LLMs without understanding what's happening behind the scenes, diving deeper into the nuts and bolts of AI will increase the effectiveness of these tools, enhance your content marketing efforts, and make your strategy more efficient. Grab your popcorn and get comfortable as we take a journey backstage with Large Language Models.
What is a large language model?
A large language model (LLM) is an algorithm that uses deep learning neural networks to ingest and analyze massive text-based datasets to produce new content. LLMS are typically tens of gigabytes in size and have billions of parameters. They fall under the umbrella of generative AI, which also includes models that can create images, videos, and other types of media.
LLMs have been around for some time but were popularized in late 2022 when the conversational AI tool ChatGPT was released to the public. ChatGPT’s rapid rise to fame is often attributed to its versatility, accessibility, and ability to engage in human-like ways.
Top Four Most Popular Generative AI LLMs
ChatGPT has taken the world by storm. So much so that some content marketers who have jumped on board don’t even realize there are other conversational AI LLMs to choose from. Here’s a quick look at the top five biggest, most popular ones.
ChatGPT by OpenAI
Starting with the most familiar, ChatGPT is an open-source AI chatbot powered by the GPT-3.5 (with optional access to GPT-4) language model. It is capable of engaging in natural language conversations with users. ChatGPT is trained on a wide array of topics and can assist with various tasks like answering questions, providing information, and generating headlines, outlines, and creative content — and much more. It's designed to be friendly and helpful and can adapt to different conversational styles and contexts.
LaMDA by Google
LaMDA is a family of transformer-based models specialized for dialog. These AI models are trained on 1.56T words of public dialog data. LaMBDA can engage in free-flowing conversations on a wide array of topics. Unlike traditional chatbots, it is not limited to pre-defined paths and can adapt to the direction of the conversation.
PaLM by Google
PaLM is a language model capable of handling various tasks, including complex learning and reasoning. It can outperform state-of-the-art language models and humans in language and reasoning tests. The PaLM system uses a few-shot learning approach to generalize from small amounts of data, approximating how humans learn and apply knowledge to solve new problems.
Llama by Meta
Llama is a text-to-text transformer model trained on a wide range of datasets covering multiple languages. Llama is capable of achieving state-of-the-art performance on many cross-lingual natural language processing (NLP) tasks.
There are, of course, many more LLMs on the market, like Google Bard and Microsoft Bing — and the number grows by the day. On top of that, technology leaders are baking AI and chatbots into products like M365 Copilot, Salesforce Einstein, and Google Docs.
How are LLMs like ChatGPT used in marketing?
Now that you have an overview of the large language model landscape, let’s talk about how ChatGPT by OpenAI and similar LLMs have the potential to make a significant impact on marketing content creation and engagement. These AI tools can understand, generate, and predict content, which is useful for marketers across a variety of functions. A few of the most popular uses of LLMs by marketers include:
Generating blog post ideas
When you have a topic or keyword you want to build content around, LLMs are incredibly helpful in brainstorming blog post ideas. They can provide a diverse range of suggestions based on your topic and target audience, enabling you to create unique, compelling blog posts.
Developing blog outlines
LLMs can help you organize your thoughts and ideas by generating structured content frameworks. They can also create detailed outlines that you can then restructure, rework, or expand on so your final outline reflects the purpose and goals of the content piece.
Writing social media posts
Because LLMs conduct sentiment analysis as part of their algorithm, they can generate engaging, contextually relevant content based on the topic, audience, and voice of your brand. With the instruction and context you provide, LLMs quickly write captivating posts, increasing social media engagement.
Developing a marketing strategy
Generally speaking, the challenge of creating a marketing strategy is best left to human brains. But LLMs can do a lot to assist in this process. They can provide a list of elements your strategy should include, answer questions about your target market, cross-check your existing strategy for missing pieces, and provide insightful suggestions and creative ideas based on your goals, target audience, and industry trends.
Building target audience profiles
LLMs can use their own knowledge, coupled with internet browsing, to generate detailed buyer personas based on demographic data, consumer behaviors, and interests of your target audience. They can write a first draft of your audience profiles, which you can then hone and perfect as required.
LLM Basics for Content Marketers
Most content marketers don’t need to understand how neural networks work or become experts in machine learning. It might be helpful, however, for you to have a basic understanding of LLMs and advancements in the technology, so you can better understand their strengths and weaknesses — and even leverage different types of LLMs for different use cases.
Understanding these technical aspects of how large language models work can help you use these tools more effectively and catch them when they glitch.
Parameters
In the context of machine learning and LLMs, parameters are the parts of the model learned from historical training datasets. Think of parameters as the brain cells of our model. They're the bits that learn from all the data fed into the model during training. Essentially, they're the model's memory, storing all the knowledge it's learned.
The most common types of parameters are weights and biases in the model's layers. Weights determine the strength of the connection between two nodes in the neural network, while biases allow the model to adjust its output independently of its input. These are adjusted during the training process to minimize the difference between the model's predictions and the actual outcomes.
The number of parameters in AI models is a bit like the ingredients in a recipe — they can have a significant impact on the output. More parameters allow the model to capture more complex relationships in the data, which can lead to better performance. On the other hand, too many parameters can lead to overfitting, where the model becomes a know-it-all on its training data but a novice when it comes to new, unseen datasets.
In LLMs like GPT-3.5, the parameters include the weights and biases in the model's transformer layers. These parameters allow the model to understand the context of words in a sentence, the grammar of the language, and other complex relationships in the text.
Here's why this matters to marketers: Given the large number of parameters in LLMs (often in the billions), managing and training these models is like juggling a lot of balls at once, requiring some serious computational muscle. That's why it’s valuable for marketers to write clear, detailed prompts and accomplish one objective at a time. With billions of dots to connect, you’ll want to make your LLM’s job as easy as possible.
Transformers
Transformers (not to be confused with the self-changing robot ilk) are a type of model architecture used in many LLMs, including GPT-3.5. They're built to handle data that comes in a sequence, like the words in a sentence or the lyrics in a song.
Transformers have something called an "attention" mechanism. It's like the brain of the model, weighing which words are important when it's generating each word in the response. This means transformers can take in the whole context of a piece of text in one go rather than one word at a time.
Transformers consist of two main parts:
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The encoder - reads and interprets the input text
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The decoder - generates the output text
In some models, only the decoder is used, while in others, only the encoder is used.
Why this matters for marketers: Because transformers see the entire context of the text input, they can sometimes generate text that is thematically consistent but factually incorrect, as they don't have a source of truth beyond the patterns they've learned in their training data. For this reason, it's important that all AI-generated content be fact-checked by a human.
Neural network layers
Neural networks, the underlying technology for LLMs, are composed of layers of artificial neurons or nodes. These layers are categorized into three types, as follows.
Input Layer
Think of the input layer as the front door of the neural network. It's where all the data first walks in to be processed. In the case of text data, this could be words or sentences that you want the model to learn from. It's like the model's first impression of the data, so it plays a pretty important role in setting the stage for all the learning that's about to happen.
Hidden Layers
After the data has walked through the front door, it encounters a bustling group of layers inside — that's your hidden layers. These are the layers between the input and output layers, which each pick up different patterns and connections in the data and apply a set of weights and biases. They're called "hidden" because we don't see what's going on inside them directly, but we know they're responsible for understanding the context, grammar, and semantics of the input text.
Output Layer
After the data has made its grand entrance through the input layer and pin-balled through the hidden layers, it lands at the output layer. This is the final stop, the grand finale of our neural network journey. The output layer provides the answer to the given inputs after processing through the network and delivers something we can use.
Every layer in a neural network is like a building block, helping the model learn from the data it's fed. The more layers, the deeper and more complex the model, which is why LLMs can whip up text that sounds pretty close to human language. However, it's important to note that while having more layers can increase a model's capacity to learn complex patterns, it can also make the model more prone to overfitting and harder to train.
Marketers are most concerned about the input layer and the output layer. However, it’s important to be aware of how your input affects both the hidden layers and the output layer.
Why this matters to marketers: LLMs respond incredibly well to step-by-step, simple directions. Resist the urge to type in stream-of-consciousness paragraphs, and be prepared to correct and redirect your chatbot to get closer to the result you want.
How LLMs are trained
While the interface of a large language model like ChatGPT is very simple, developing prompts and understanding the output you might receive is not. A deeper understanding of how these AI models are trained can help you:
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Plan better, more effective inputs
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Maintain reasonable expectations about how the LLM can help you
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Understand the ethical implications of LLMs, such as the potential for bias, inaccuracy, and plagiarism
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Select the right model for your goals or even train your own
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Troubleshoot any issues you encounter with the output you receive
Training LLMs is a complex, nuanced process, and it’s safe to say that no two LLMs are trained the same way. But here’s a broad overview of how the training process works.
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Data collection
The first step in training LLMs is to collect a large amount of text datasets. This data can come from a variety of sources, such as books, websites, and other texts. The goal is to expose the model to a wide range of language usage, styles, and topics. Generally speaking, the more data you have, the more intelligent and accurate the LLM will be. However, there is also a risk of overtraining, particularly if the training set is relatively homogeneous.
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Preprocessing
The collected data is then preprocessed to make it suitable for training. This can involve cleaning the data, removing irrelevant information, and converting the text into a format that the model can understand using a language model like Bidirectional Encoder Representations from Transformers (BERT).
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Model architecture selection
The architecture of the model, such as transformer architecture, RNN, or CNN, is chosen based on the specific requirements of the task. The architecture defines the structure of the neural network, including the number of layers in the network and the connections between them. Transformers are excellent for text generation because they can see context, RNNs are ideal for translation tasks because they crunch data sequentially, and CNNs are great for image generation because they can detect local patterns.
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Training
The actual training process involves feeding the preprocessed data into the model and using a machine-learning model to train it. The model detects and “learns” the patterns and relationships in each new dataset and generates outputs accordingly. A data scientist feeds additional data and uses AI learning techniques to adjust the model's parameters (weights and biases) to optimize the output it produces. The goal is to minimize the difference between the model's predictions and the actual data, a measure known as “loss.”
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Evaluation and fine-tuning
After the initial training, the model is evaluated on a separate set of data, known as the validation set. This helps check if the model is generalizing well or if it's overfitting to the training data. Based on the performance of the validation set, the model might be fine-tuned further by adjusting its parameters or the hyperparameters of the training process.
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Testing
Finally, the model is tested on a test set, another separate set of data that it hasn't seen during training or validation. This gives a final measure of how well the model is likely to perform on unseen data.
Leveraging LLMs and Chatbots in Content Marketing
As we wrap up our behind-the-scenes looks at the world of Large Language Models, it's clear that these AI powerhouses are more than just a passing trend. They're transforming the content marketing landscape, making our jobs easier and our content more engaging and effective.
But, as with any tool, understanding how to use LLMs properly is key. What you’ve learned here about the complex process of building and training LLMs, their strengths and limitations, and their important ethical considerations is instrumental in the fine-tuning of your usage and prompting.
Many content marketers in your shoes are looking for effective tools to use generative AI for interesting, informative, human-like content. At Scripted, we believe that when humans and AI come together, amazing marketing content is the result. That’s why we have memberships for human writing and human and AI-assisted writing — and now we’ve added a new membership just for AI-generated content.
This membership gives you access to our GPT-4 chatbot, Scripted Copilot, AI-driven blog ideas, and copy generators for everything from headlines to infographics. You can start powering your content with AI today by signing up for a 30-day trial of Scripted here.
So, here's to the future of content marketing, a future where AI and human creativity go hand in hand. Let's embrace the power of Large Language Models and see where this exciting journey takes us.
