eDiscovery Best Practices for Effective Use of Generative AI and LLMs

eDiscovery Best Practices for Effective Use of Generative AI and LLMs

Generative AI (GenAI) and large language model (LLM) technology continue to transform eDiscovery in 2024. While these tools show promise in streamlining eDiscovery review, an ACEDS article by Daniel Bonner says the effective use of generative AI hinges on best practices and sound approaches.

Bonner notes that GenAI’s perceived ease of use, compared to keyword searches, is deceptive. Poorly crafted prompts can yield irrelevant or insufficient results, just like ineffective keyword searches. Prompt engineering involves refining queries iteratively to communicate effectively with AI models and achieve desired outcomes. For instance, starting with a general query about corporate internal investigations can be refined into specific questions to obtain more focused responses.

According to the article, a successful review process, whether using keyword searches, Technology Assisted Review (TAR), or GenAI, requires clear parameters and objectives, a comprehensive review plan, and defined protocols and guidelines. Training the review team, conducting iterative reviews, implementing quality control checks, and thorough documentation are crucial steps.

Preparation and validation are essential regardless of the technology used. Reviewers must understand the legal issues, develop a detailed plan, and execute and validate the process meticulously. This includes establishing rigorous quality control procedures, conducting statistical sampling, and documenting the review process thoroughly.

GenAI, like its predecessor TAR, isn’t a magic solution. Effective use of generative AI requires a well-planned, efficiently conducted, and thoroughly validated review process. Technology evolves, but best practices for review remain constant.