Making your AI sound like you

"More AI generated nonsense…" — sound familiar?

You’ve likely come across an email or report that reeks of the notoriously generic AI-generated content produced by tools like ChatGPT. These models are designed to be universal, meaning they often miss the unique nuances of your personal voice and tone. Fortunately, with a few targeted techniques, you can overcome this limitation.

It’s important to distinguish between altering a model’s style and enhancing its knowledge. These two objectives require fundamentally different approaches, and understanding this distinction is key for business owners, developers, and AI professionals alike.

Let’s explore the primary methods for personalizing large language models and the trade-offs each approach entails.

Retrieval-Augmented Generation (RAG): Bringing in External Knowledge

One of the easiest ways to give an LLM access to new information is through Retrieval-Augmented Generation (RAG). This approach allows the model to pull in?external, specific, and controllable knowledge?without altering its core weights.

How RAG Works

• The model retrieves relevant documents, databases, or other structured data before adding it to its context window and generating the response.
• This method ensures that the AI has access to the latest and most relevant information without modifying its underlying architecture.
• Examples include chatbots that reference legal documents, customer support AI pulling from company policies, or research assistants fetching scientific papers.

Pros & Cons of RAG

??Flexible & Updatable?– You can easily change the external data source without retraining the model.

??More control over knowledge?– Ensures responses are grounded in accurate, specific data, reducing halucinations.

??Does not change the model’s behaviour?– The AI still writes and speaks in its original style, even when given new knowledge.

Important Note: While RAG is excellent for providing?specific and controllable knowledge, it does not alter how the model structures its responses or makes decisions.

Fine-Tuning: Rewriting the Model’s Internal Knowledge

Fine-tuning is one of the most powerful personalisation techniques because it?modifies the model’s internal weights—essentially rewiring its brain to incorporate new information or adjust its speaking style.

How Fine-Tuning Works

• The model is further trained on a carefully curated dataset containing desired responses, examples, or corrections.
• This allows the AI to adapt not only its knowledge but also its tone, formatting, and preferred response style.
• Fine-tuning can be done on open-source models (like LLaMA, Mistral, or Falcon) or via APIs like OpenAI’s fine-tuning option.

Pros & Cons of Fine-Tuning

??Changes both knowledge and response style?– The AI learns to adopt the preferred way of speaking.

??Can specialize in domain-specific expertise?– Useful for legal, medical, or highly technical applications.

??Requires compute resources & expertise?– Needs data preparation and training infrastructure.

??Not as easily updatable?– Once fine-tuned, new knowledge must be incorporated with additional training rounds.

Fine-tuning is ideal when you need to?change the way an AI speaks while also integrating domain-specific knowledge.

System Prompts: Immediate Control Over Behaviour

For users who need to influence?how an AI behaves without modifying its internal knowledge,?system prompts provide a simple and effective approach.

How System Prompts Work

• A system prompt is a special instruction given to the model at the beginning of an interaction.
• It defines the AI’s role, tone, and behavioral constraints.
• Example: "You are a legal assistant. Always provide concise, formal answers using legal citations."

Pros & Cons of System Prompts

??Fast & easy?– No retraining is required; just adjust the input prompt.

??Highly adaptable?– Can switch tones, roles, and instructions instantly

??Does not change core knowledge?– The AI still pulls from its existing model data.

??Can be inconsistent?– Some models may override or ignore system prompts in certain contexts.

This won't lead to crazy improvements, but it's an easy way to improve the model's behaviour quickly, so why wouldn't you do it?

Reinforcement Learning (RL) and Custom Training: The Deepest Level of Personalisation

At the lowest level of customisation, we have?training an LLM from scratch?or using?Reinforcement Learning (RL) methods?to adjust the model’s long-term behaviour.

How Custom Training & RL Work

• Instead of modifying an existing model, we can pick an architecture, do the pre-training on the data we choose and then use custom RL-based post-training methods.

Pros & Cons of RL & Custom Training

??Ultimate control?– Can develop highly specialised, proprietary AI solutions, giving you a tangible advantage over competitors.

??Long-term adaptability?– The model can be shaped to follow specific business rules and compliance standards.

??Expensive & resource-intensive?– Requires extensive datasets, computational power, and AI expertise. However, the price of doing this is going down by the day.

Custom training from scratch is not the best path for most businesses, unless they have specific requirements or unique constraints. Instead, fine-tuning, system prompts, and RAG offer more practical alternatives.

Key Takeaways:

1. Know your goal

• If you want to?change the knowledge?an AI model has, use?RAG?or?fine-tuning.
• If you want to?change how the AI speaks, use?system prompts?or?fine-tuning.
• If you want?ultimate control,?RL/custom training?is an option but not usually necessary.

2. Use the most efficient method

• System prompts are?fast & cheap?for behaviour control.
• RAG is great for?external knowledge retrieval.
• Fine-tuning is the?best balance of knowledge + style adaptation.
• RL and custom training are?only for deep, research-level AI projects.

3. Personalisation is a layered process

• Many AI solutions use a?combination?of these techniques.
• Example: A chatbot might use?RAG?for up-to-date information, a?system prompt?for tone, and?fine-tuning?for industry-specific knowledge.

Conclusion

Whether you are an individual trying to improve the quality of the content you generate for your daily tasks, or you are a business looking to automate workflows, having models that are personalised for you is a must.

If you need help customising your model or automating any other task don't hesitate to reach out to me.

Happy Monday and enjoy your week.