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To scale ground truth generation and curation, you can apply a risk-based approach in conjunction with a prompt-based strategy using LLMs. Its important to note that LLM-generated ground truth isnt a substitute for use case SME involvement. To convert the source document excerpt into ground truth, we provide a base LLM prompt template.
Cost-effective – The solution should only invoke LLM to generate reusable code on an as-needed basis instead of manipulating the data directly to be as cost-effective as possible. LLMs excel at writing code and reasoning over text, but tend to not perform as well when interacting directly with time-series data.
RAG is a methodology to improve the accuracy of LLM responses answering a user query by retrieving and inserting relevant domain knowledge into the language model prompt. Tuning chunking and indexing in the retriever makes sure the correct content is available in the LLM prompt for generation.
Data Foundation on AWS Amazon S3: Scalable storage foundation for data lakes. AWS Lake Formation: Simplify the process of creating and managing a secure data lake. Amazon Redshift: Fast, scalable data warehouse for analytics. AWS Glue: Fully managed ETL service for easy data preparation and integration.
Data Foundation on AWS Amazon S3: Scalable storage foundation for data lakes. AWS Lake Formation: Simplify the process of creating and managing a secure data lake. Amazon Redshift: Fast, scalable data warehouse for analytics. AWS Glue: Fully managed ETL service for easy data preparation and integration.
Read triples from the Neptune database and convert them into text format using an LLM hosted on Amazon Bedrock. Amazon Bedrock Knowledge Bases is configured to use the preceding S3 bucket as a data source to create a knowledge base. The following LLM models must be enabled. account } WHERE { ?asset asset "GlueTableAssetType".
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