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Why You Should Do Data Cleaning Before Training an LLM

Data cleaning is a central task in any data-science project, but people tend to forget it espacially in the LLM landscape. Lettria can help.

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In the realm of AI, particularly when dealing with Large Language Models (LLMs), the adage “garbage in, garbage out” holds profoundly true. The quality of data input determines the quality of the model’s output. This is where the critical process of data cleaning comes into play.

This article delves into the why and how of data cleaning, emphasizing its indispensable role in the realm of LLMs.

Understanding the Imperative of Data Cleaning

Here's why it matters:

Data cleaning, in its essence, involves refining, correcting, and organizing raw data. It’s a preparatory step, yet, its impact is long-term and far-reaching in model training.

Why Data Cleaning is Non-Negotiable

  1. Enhances Model Accuracy: Clean data means the LLM can learn patterns and language more effectively.
  2. Reduces Bias: By removing inappropriate or irrelevant data, the risk of perpetuating biases is significantly reduced.
  3. Saves Time and Resources: A well-cleaned dataset means less time spent on troubleshooting and recalibrating the model post-deployment.
  4. Improves Decision-making: For businesses, clean data ensures that the insights and decisions driven by the LLM are based on accurate information.

How to Approach Data Cleaning

  1. Identify and Remove Irrelevant Data: Focus on data that is relevant to the specific task of the LLM.
  2. Correct Errors and Inconsistencies: Look for typographical errors, duplicates, or misplaced entries.
  3. Standardize Data Formats: Consistency in data formats aids in smoother processing by the LLM.
  4. Handle Missing Data: Decide on a strategy for dealing with gaps in the data – whether to fill them, remove them, or mark them.
  5. Anonymize Sensitive Information: Ensure compliance with privacy regulations by anonymizing or removing sensitive data.

Lettria’s Data Cleaning Approach

Thankfully, when you use a tool like Lettria, the data cleaning process is built in — greatly reducing the complexity involved in doing the cleaning process yourself, and allowing you to focus on more important aspects of your project rather than collecting and cleaning data.

Our specialised cleaning formula is applied to every data type uploaded to Lettria, making it easy to manage cluttered data such as speech-to-text transcripts. This makes sure that

Moreover, you can easily manage all your databases in Lettria, ensuring that you are able to regularly audit your data.

Impact of Data Cleaning Across Industries


Data cleaning in healthcare ensures accurate and unbiased assistance in diagnosis, improving patient outcomes.

It’s even said to be critical to clinical interoperability, and mismatched patient and lab data can cause disastrous effects to healthcare systems.


In the field of finance, particularly in Fraud Detection Systems, data cleaning enhances the precision of anomaly detection, safeguarding against financial fraud.

The difference between clean and unclean data can mean millions of dollars of losses due to undetected fraud.


Retail companies use data for personalised marketing — and clean data leads to more accurate customer profiles, thereby improving targeted marketing efforts.


In the field of education, data cleaning facilitates the creation of personalized learning paths based on clean and relevant educational data.


For document analysis and research, data cleaning assists in more accurate and efficient legal research by feeding the LLM relevant legal texts.

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Challenges and Solutions in Data Cleaning


  1. Volume of Data: The sheer amount of data can be overwhelming.
  2. Diverse Data Sources: Data comes from varied sources, making standardization difficult.
  3. Complexity of Data: Especially in industries like healthcare, the complexity of data can be daunting.


  1. Automated Tools: Employ automated data cleaning tools to handle large volumes.
  2. Standardization Protocols: Develop and adhere to standardization protocols for diverse data.
  3. Expert Involvement: In complex fields, involve domain experts in the cleaning process.

Best Practices for Effective Data Cleaning

  1. Continuous Process: Treat data cleaning as an ongoing activity, not just a one-time task. Lettria does this by default.
  2. Document the Process: Keep a record of the data cleaning process for transparency and future reference.
  3. Quality Over Quantity: Focus on the quality of data, rather than just the quantity.
  4. Regular Audits: Regularly audit the data to ensure its cleanliness and relevance.
  5. Feedback Loop: Implement a feedback loop where the outputs of the LLM are used to further refine the data.


Data cleaning, though seemingly a preliminary step, is foundational in the training of Large Language Models. It's a critical investment that pays dividends in the form of accurate, unbiased, and effective AI tools.

Whether in healthcare, finance, retail, education, or legal industries, the meticulous practice of data cleaning ensures that the LLMs deployed are not just powerful but are also reliable and responsible tools of the future.

If you’re interested in understanding how to build your own ChatGPT-like chatbot on your business’ own data, please reach out to us and we’ll help you out!


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