Garbage In, Hallucinations Out: How Clean Data Drives LLM Performance
Everyone is talking about which LLM to use. Few are talking about what you feed it.
The AI conversation in most enterprises centers around model selection — GPT-4 versus Gemini, open-source versus proprietary, fine-tuned versus off-the-shelf. These are reasonable questions. But they're the wrong starting point. Because the single biggest variable in how well a generative AI system performs in production is not the model. It's the data going into it.
This isn't a new idea in data circles. But it hasn't fully landed in the AI conversation yet. So let's make it concrete.
Why LLMs Fail in the Real World
Large language models fail in production for many reasons — prompt design, context window limits, retrieval mismatches. But the most persistent, least glamorous cause is data quality.
LLMs generate more hallucinations when trained on or grounded by incomplete, biased, or low-quality datasets. Research from Stanford found that even domain-specific...
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