Traditional QC: Stability in the Process
When document review was fully human-driven, QC relied on a consistent set of practices: sampling, validation, independent second-pass review, human oversight, and well-established recall and precision benchmarks — the same statistical measures still used today to evaluate review accuracy and completeness. These mechanisms created defensibility, repeatability, and transparency long before any form of AI entered the workflow.
When TAR and CAL models emerged, they introduced machine-learning automation, but the QC fundamentals remained unchanged. Review teams still relied on control sets, iterative sampling, and continuous validation to confirm model performance. As one TAR white paper explains, these systems “identify and prioritize the documents most likely to be relevant,” while review managers continue to assess “the review team’s coding accuracy and understanding of the review protocol.” The core takeaway is clear: even with sophisticated automation, human-driven QC remains central, anchored by the same recall, precision, and validation standards that have guided defensible review for more than a decade.
GenAI Enters the Review Room
Now we’re seeing more use of GenAI in document review: tools that may summarize, prioritize, suggest responsive documents, or even draft review outcomes. Yet, the QC framework stays largely the same: sampling, validation, human oversight, metrics of recall and precision, and explanation of how decisions were made. For example, one industry piece reminds us: “Like TAR tools, Gen AI can . . . provide an enhanced quality control layer for attorney document review . . . Disagreements indicate where senior attorneys should look for issues.” (Consilio) And another noted that GenAI has improved the speed and reliability of QC particularly for privilege and responsiveness tagging. (ACEDS)
Clarifying: AI ≠ GenAI
It’s critical to differentiate: Not all AI is GenAI. Continuous Active Learning are AI processes (clustering and concept search-driven machine-learning, classification, ranking, iterative training) that have been accepted in the legal industry and courts for years. For instance, case-law on TAR and CAL is robust, noting the importance of sampling, recall/precision validation, and transparency. (Everlaw)
GenAI, by contrast, introduces new modes (e.g., natural-language generation, summarization, reasoning) and with that, new risk vectors (such as hallucinations). The mechanics of TAR/CAL still operate in well-structured workflows; GenAI is more exploratory, less mature in this context. One article explains: “Unlike TAR systems . . . generative AI tools . . . can process natural language instructions . . . That capability brings both opportunity and risk.” (CSDISCO)
Think of GenAI as a force-multiplier. It replaces some of the brute force, first-pass attorney review teams, but none of the QC that goes along with them.
- Human reviewers set the protocol, define metrics, and establish the gold standard of coding.
- GenAI may flag, summarize, cluster or prioritize documents; it may even perform initial classification.
- QC teams then validate: sampling reviews, second-pass verification, measuring recall/precision against agreed thresholds.
- Discrepancies or “hallucinations” (in GenAI’s case) trigger root-cause investigation, retraining of models or additional human review.
Sustaining the QC Metrics You Already Know
The idea is simply this: no matter how advanced the tech, a human remains in the loop. The QC system doesn’t vanish — it simply adapts, but the core remains remarkably consistent.
Metrics such as recall (what percentage of relevant documents were found) and precision (what percentage of documents marked relevant truly are) continue to underpin defensibility. (ComplexDiscovery) Whether review is human, TAR, CAL, or GenAI-augmented, you still need to monitor and document those metrics. The only difference is: the tech may change how you achieve your threshold, but not that you need to achieve it.
At Lucent we stand at the intersection of expertise, process and innovation. We know that whether you’re using human reviewers, TAR, CAL or GenAI-augmented workflows, our commitment to clarity, insight and senior-level attention doesn’t change.