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AI doesn’t work without data, it works with the right data
When most people think about AI, they picture clever outputs: images, text, recommendations. But what actually powers that output? Heidi Anthonis explains:
Data is the fuel behind the reasoning engine. You can prompt ChatGPT all day, but if you don’t give it context, it’ll just give you middle-of-the-road answers.”
Heidi Anthonis
Chief Innovation Officer, Happy Horizon
Large language models like GPT-4 are trained on massive, publicly available datasets; think Wikipedia, Reddit, news articles. While this makes them great at general tasks, they fall short when it comes to delivering outputs tailored to your brand voice, product range, or internal processes. Without proprietary context, AI simply can’t understand your business well enough to deliver meaningful results.
The limitations of generic AI models and the importance of proprietary data
Trusted voices in the industry echo this view. According to a recent IBM blog, proprietary data offers a unique edge: it reflects your inventory peaks, your billing logic, and how your team defines key metrics. Enterprises that leverage proprietary data in generative AI show markedly better results, not merely by adopting AI, but by customizing it with relevant internal data. Bottom line? While public models might know language, your data knows your business.
Forbes reinforces this perspective, noting that publicly available and synthetic data are no longer enough to set models apart. As the AI industry reaches saturation, exclusive, high-quality datasets have become the key to true differentiation as companies who fine-tune AI models with domain-specific knowledge, are able to outperform generic models trained on public data.
How to merge models with your data: RAG & Fine-tuning
To turn generic AI into something truly valuable for your business, you need to connect it to your own data. Otherwise, you’ll be stuck with default responses that lack nuance, accuracy, or relevance. There are two established approaches to bridge the gap between general-purpose models like GPT-4 and your proprietary data: Retrieval-Augmented Generation (RAG) and fine-tuning.
1. Retrieval-Augmented Generation (RAG)
RAG is one of the most effective, and accessible, ways to give AI access to your knowledge without modifying the underlying model. It works by indexing your data, such as documents, manuals, product info, or FAQs, and retrieving only the relevant content in real time whenever the model is prompted.
- Advantage: No need to re-train the model, which saves time and compute costs.
- Benefit: Greatly reduces hallucinations and off-topic responses by grounding answers in your actual context.
RAG is especially useful for customer support chatbots, internal knowledge bases, and marketing teams that want AI to stay on-brand and accurate. According to Meta AI, RAG models significantly outperform vanilla LLMs on question-answering tasks when backed by domain-specific sources.
2. Fine-Tuning / Custom GPTs
Fine-tuning goes a step further. Instead of simply referencing your data, you train the model on it. That means feeding it labeled examples, structured data, or domain-specific prompts so it learns patterns directly relevant to your workflows.
- Advantage: Boosts performance on niche or technical tasks, such as legal contract drafting, medical diagnosis summaries, or ERP-specific automation.
- Trade-off: Requires more effort, expertise, and careful data curation to avoid model drift or overfitting.
OpenAI and other providers now allow fine-tuning on smaller custom GPTs, making this approach more accessible to mid-sized businesses; especially those in regulated or high-context industries.
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