Data Cleaning & Preparation for LLM Training on AWS

The Data Cleaning & Preparation for LLM Training Assessment helps organizations prepare high-quality, trusted datasets for training, fine-tuning, and retrieval-augmented generation (RAG) workflows. Delivered by senior AWS and AI specialists, this engagement evaluates data quality, structure, governance, and security to ensure your data is ready for use with large language models on AWS.

During the assessment, Cloud Catalysts reviews structured and unstructured data sources, including documents, transcripts, logs, and knowledge bases. We evaluate data completeness, accuracy, duplication, labeling, and relevance, while identifying issues that commonly degrade LLM performance such as noise, inconsistencies, and sensitive data exposure. The assessment also reviews data ingestion pipelines, preprocessing steps, and storage patterns to ensure scalability and cost efficiency.

Customers receive a clear data preparation strategy aligned with AWS-native services and GenAI best practices. The result is a practical roadmap to improve model accuracy, reduce hallucinations, and enable secure, compliant LLM training or RAG implementations to be used within AWS BedRock.