Integrating large language models (LLMs) with existing enterprise systems, such as CRMs and ERPs, presents significant challenges.

Data quality: a crucial obstacle

Despite the relative simplicity of connecting to systems, the real difficulty emerges when analyzing data quality. Fields with cryptic names, complex interconnected tables, and outdated documentation are common problems. This situation leads LLMs to generate incorrect answers because they cannot correctly interpret the meaning of business data.

Strategies to improve integration

To overcome these challenges, several strategies can be adopted, including:

  • Manual metadata tagging: Manually assign descriptive tags to metadata to facilitate data interpretation by LLMs.
  • Knowledge graphs: Create knowledge graphs to represent the relationships between different data elements.
  • Advanced prompt engineering: Develop specific and well-structured prompts to guide LLMs in interpreting data.

Careful data preparation and the implementation of targeted strategies are essential to fully exploit the potential of LLMs in the enterprise environment.