Understanding the LLM Twin concept

The first step is to have a clear vision of what we want to create and why it’s valuable to build it. The concept of an LLM Twin is new. Thus, before diving into the technical details, it is essential to understand what it is, what we should expect from it, and how it should work. Having a solid intuition of your end goal makes it much easier to digest the theory, code, and infrastructure presented in this book.

What is an LLM Twin?

In a few words, an LLM Twin is an AI character that incorporates your writing style, voice, and personality into an LLM, which is a complex AI model. It is a digital version of yourself projected into an LLM. Instead of a generic LLM trained on the whole internet, an LLM Twin is fine-tuned on yourself. Naturally, as an ML model reflects the data it is trained on, this LLM will incorporate your writing style, voice, and personality. We intentionally used the word “projected.” As with any other projection, you lose a lot of information along the way. Thus, this LLM will not be you; it will copy the side of you reflected in the data it was trained on.

It is essential to understand that an LLM reflects the data it was trained on. If you feed it Shakespeare, it will start writing like him. If you train it on Billie Eilish, it will start writing songs in her style. This is also known as style transfer. This concept is prevalent in generating images, too. For example, let’s say you want to create a cat image using Van Gogh’s style. We will leverage the style transfer strategy, but instead of choosing a personality, we will do it on our own persona.

To adjust the LLM to a given style and voice along with fine-tuning, we will also leverage various advanced retrieval-augmented generation (RAG) techniques to condition the autoregressive process with previous embeddings of ourselves.

We will explore the details in Chapter 5 on fine-tuning and Chapters 4 and 9 on RAG, but for now, let’s look at a few examples to intuitively understand what we stated previously.

Here are some scenarios of what you can fine-tune an LLM on to become your twin:

• LinkedIn posts and X threads: Specialize the LLM in writing social media content.
• Messages with your friends and family: Adapt the LLM to an unfiltered version of yourself.
• Academic papers and articles: Calibrate the LLM in writing formal and educative content.
• Code: Specialize the LLM in implementing code as you would.

All the preceding scenarios can be reduced to one core strategy: collecting your digital data (or some parts of it) and feeding it to an LLM using different algorithms. Ultimately, the LLM reflects the voice and style of the collected data. Easy, right?

Unfortunately, this raises many technical and moral issues. First, on the technical side, how can we access this data? Do we have enough digital data to project ourselves into an LLM? What kind of data would be valuable? Secondly, on the moral side, is it OK to do this in the first place? Do we want to create a copycat of ourselves? Will it write using our voice and personality, or just try to replicate it?

Remember that the role of this section is not to bother with the “What” and “How” but with the “Why.” Let’s understand why it makes sense to have your LLM Twin, why it can be valuable, and why it is morally correct if we frame the problem correctly.

Why building an LLM Twin matters

As an engineer (or any other professional career), building a personal brand is more valuable than a standard CV. The biggest issue with creating a personal brand is that writing content on platforms such as LinkedIn, X, or Medium takes a lot of time. Even if you enjoy writing and creating content, you will eventually run out of inspiration or time and feel like you need assistance. We don’t want to transform this section into a pitch, but we have to understand the scope of this product/project clearly.

We want to build an LLM Twin to write personalized content on LinkedIn, X, Instagram, Substack, and Medium (or other blogs) using our style and voice. It will not be used in any immoral scenarios, but it will act as your writing co-pilot. Based on what we will teach you in this book, you can get creative and adapt it to various use cases, but we will focus on the niche of generating social media content and articles. Thus, instead of writing the content from scratch, we can feed the skeleton of our main idea to the LLM Twin and let it do the grunt work.

Ultimately, we will have to check whether everything is correct and format it to our liking (more on the concrete features in the Planning the MVP of the LLM Twin product section). Hence, we project ourselves into a content-writing LLM Twin that will help us automate our writing process. It will likely fail if we try to use this particular LLM in a different scenario, as this is where we will specialize the LLM through fine-tuning, prompt engineering, and RAG.

So, why does building an LLM Twin matter? It helps you do the following:

• Create your brand
• Automate the writing process
• Brainstorm new creative ideas

What’s the difference between a co-pilot and an LLM Twin?

A co-pilot and digital twin are two different concepts that work together and can be combined into a powerful solution:

• The co-pilot is an AI assistant or tool that augments human users in various programming, writing, or content creation tasks.
• The twin serves as a 1:1 digital representation of a real-world entity, often using AI to bridge the gap between the physical and digital worlds. For instance, an LLM Twin is an LLM that learns to mimic your voice, personality, and writing style.

With these definitions in mind, a writing and content creation AI assistant who writes like you is your LLM Twin co-pilot.

Also, it is critical to understand that building an LLM Twin is entirely moral. The LLM will be fine-tuned only on our personal digital data. We won’t collect and use other people’s data to try to impersonate anyone’s identity. We have a clear goal in mind: creating our personalized writing copycat. Everyone will have their own LLM Twin with restricted access.

Of course, many security concerns are involved, but we won’t go into that here as it could be a book in itself.

Why not use ChatGPT (or another similar chatbot)?

This subsection will refer to using ChatGPT (or another similar chatbot) just in the context of generating personalized content.

We have already provided the answer. ChatGPT is not personalized to your writing style and voice. Instead, it is very generic, unarticulated, and wordy. Maintaining an original voice is critical for long-term success when building your brand. Thus, directly using ChatGPT or Gemini will not yield the most optimal results. Even if you are OK with sharing impersonalized content, mindlessly using ChatGPT can result in the following:

• Misinformation due to hallucination: Manually checking the results for hallucinations or using third-party tools to evaluate your results is a tedious and unproductive experience.
• Tedious manual prompting: You must manually craft your prompts and inject external information, which is a tiresome experience. Also, the generated answers will be hard to replicate between multiple sessions as you don’t have complete control over your prompts and injected data. You can solve part of this problem using an API and a tool such as LangChain, but you need programming experience to do so.

From our experience, if you want high-quality content that provides real value, you will spend more time debugging the generated text than writing it yourself.

The key of the LLM Twin stands in the following:

• What data we collect
• How we preprocess the data
• How we feed the data into the LLM
• How we chain multiple prompts for the desired results
• How we evaluate the generated content

The LLM itself is important, but we want to highlight that using ChatGPT’s web interface is exceptionally tedious in managing and injecting various data sources or evaluating the outputs. The solution is to build an LLM system that encapsulates and automates all the following steps (manually replicating them each time is not a long-term and feasible solution):

• Data collection
• Data preprocessing
• Data storage, versioning, and retrieval
• LLM fine-tuning
• RAG
• Content generation evaluation

Note that we never said not to use OpenAI’s GPT API, just that the LLM framework we will present is LLM-agnostic. Thus, if it can be manipulated programmatically and exposes a fine-tuning interface, it can be integrated into the LLM Twin system we will learn to build. The key to most successful ML products is to be data-centric and make your architecture model-agnostic. Thus, you can quickly experiment with multiple models on your specific data.