To truly benefit from AI, the quality of the input data is paramount. AI models are only as good as the data they're trained on or the data they can access (in RAG applications).
Even with our advanced algorithms and features, without well-curated and contextually accurate data, Lampi's capabilities are much more limited. It’s similar to a chef who needs quality ingredients to cook a sumptuous meal. Even the most skilled chef can't compensate for subpar ingredients. Likewise, even the most advanced AI models can't make up for poor-quality data.
The performance, accuracy, and effectiveness of an AI system are largely dependent on the quality and relevance of the data. Therefore, data curation — the process of organizing, cleaning, and enhancing raw data — becomes an indispensable step.
High-quality data leads to precise outputs, enabling better decision-making and forecasting.
As such, when integrating knowledge, data from different documents or applications must be gathered, cleansed, and carefully selected to draw out valuable insights. Irrelevant data can cloud the AI model's decision-making process and affect its ability to perform tasks.
Key steps to curate high-quality data involve notably:
- determining what quality data means in the context of your specific business requirements. This definition will differ across sectors and businesses;
- identifying relevant data sources: seek out sources that can provide the quality of data you require. Ensure these sources are reliable and consistent.
Lampi offers a user-friendly interface that allows for effortless curation and rating of data, leading to the generation of consistently relevant and improved answers.
Tips: To improve Lampi outputs, note your documents.
Tips: To improve Lampi outputs, note your documents. You can evaluate the quality of your data (on a scale of 1 to 5). We then apply an algorithmic enhancement based on the rating to improve the quality of search results (higher-rated documents will be more prominently featured and considered by Lampi)