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Generating value from enterprise data: Best practices for Text2SQL and generative AI

Generative AI has revolutionized AI, finding applications in text generation, code generation, summarization, and more. One evolving area is natural language processing (NLP) for intuitive SQL queries, aiming to make database querying more accessible to non-technical users. Key considerations include prompt engineering, architecture patterns, and optimization for efficient text-to-SQL systems using Large Language Models (LLMs). The authors shared insights into this innovative field.

Generating value from enterprise data: Best practices for Text2SQL and generative AI

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Generative AI in Text to SQL: Unlocking New Opportunities

Generative AI has opened up a lot of potential in the field of AI. We are seeing numerous uses, including text generation, code generation, summarization, translation, chatbots, and more. One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries.

Why do we need Text2SQL?

Today, a large amount of data is available in traditional data analytics, data warehousing, and databases, which may be not easy to query or understand for the majority of organization members. The primary goal of Text2SQL is to make querying databases more accessible to non-technical users, who can provide their queries in natural language.

Key components for Text to SQL

Text-to-SQL systems involve several stages to convert natural language queries into runnable SQL:

  • Natural language processing
  • SQL generation
  • Database query

Prompt engineering considerations for natural language to SQL

Effective prompt engineering is key to developing natural language to SQL systems. Clear, straightforward prompts provide better instructions for the language model. Well-designed prompts that give the model sufficient instruction, context, examples, and retrieval augmentation are crucial for reliably translating natural language into SQL queries.

Optimization and best practices

Optimization techniques can improve performance and efficiency when developing text-to-SQL systems using LLMs. Some key areas to consider include caching, monitoring, materialized views, refreshing data, and central data catalog.

Architecture patterns

Let’s look at some architecture patterns that can be implemented for a text to SQL workflow:

  • Prompt engineering
  • Prompt engineering and fine-tuning
  • Prompt engineering and RAG

Conclusion

In this post, we discussed how we can generate value from enterprise data using natural language to SQL generation. We looked into key components, optimization, and best practices. We also learned architecture patterns from basic prompt engineering to fine-tuning and RAG.

About the Authors

Randy DeFauw is a Senior Principal Solutions Architect at AWS. Nitin Eusebius is a Sr. Enterprise Solutions Architect at AWS. Arghya Banerjee is a Sr. Solutions Architect at AWS in the San Francisco Bay Area.

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