Lessons from eDiscovery for RAG: Building Trustworthy Retrieval Systems

A follow-up to "What is RAG? Retrieval-Augmented Generation Systems with Archival Sources"

by Alvin Blackshear  |  Historian & Researcher  <ablackshear@gmail.com>

Retrieval Is Not a New Problem

In my last article, I tested Retrieval-Augmented Generation (RAG) systems against archival sources and found that even well-engineered pipelines struggle with the basic question of whether they found the right material before they ever tried to answer a question. That struggle is not new. It has a name, a case history, and a professional discipline built around it: eDiscovery.

For three decades, litigators and their technical partners have grappled with a problem that RAG developers are only now rediscovering: how do you find the right documents inside an enormous, messy, heterogeneous collection, and how do you prove, after the fact, that you found them? In litigation, the collection is made of emails, contracts, scanned memos, spreadsheets, and chat logs, scattered across custodians and file formats. In RAG, the collection is made of chunked documents, embeddings, and vector indices. The materials differ. The underlying task, sorting a haystack for a small number of relevant needles under conditions of uncertainty, is the same.

This matters because the AI field tends to treat retrieval as a purely technical challenge: pick a better embedding model, tune the reranker, adjust top-k. Wu and colleagues' recent survey of RAG architectures catalogs exactly this kind of technical churn across retrieval pipelines, hybrid search strategies, reranking layers, and evaluation methods (Wu et al., 2026). What that survey does not resolve, and what most RAG engineering discussions skip entirely, is the governance question that eDiscovery has spent decades answering: how do you know your retrieval process is good enough to trust, and how do you demonstrate that to someone who was not in the room when you built it?

Precision versus Recall, Revisited

Every eDiscovery practitioner learns the same lesson early: precision and recall pull against each other, and the cost of getting the balance wrong is not abstract. Set the net too wide and reviewers drown in irrelevant documents, burning hours and budget on material that never should have surfaced. Set the net too narrow and you risk missing the one email that determines the outcome of a case. Cormack and Grossman's foundational work on Continuous Active Learning showed that recall-oriented review, iteratively refined through relevance feedback, could achieve high recall with a fraction of the manual review effort that exhaustive linear review required (Cormack and Grossman, 2015). That paper reframed retrieval not as a one-time query but as a process, one that improves through repeated rounds of human judgment and system adjustment.

RAG systems face a structurally identical tradeoff, just with different vocabulary. Top-k retrieval determines how many chunks get pulled before generation begins. Reranking determines which of those chunks survive to reach the model's context window. Filtering determines which sources are excluded before retrieval even starts. Elkiran and Rasheed's comparative study of retriever-reranker pairings makes the tradeoff explicit: different combinations of retrieval and reranking strategies produce measurably different quality and efficiency profiles, and no single configuration dominates across all use cases (Elkiran and Rasheed, 2026). That is precision versus recall wearing a different uniform. A RAG system tuned for maximum precision will answer confidently and sometimes wrongly, having missed the one chunk that would have changed the answer. A system tuned for recall will surface enough context to be safe, but risks diluting the model's attention with irrelevant material, an effect not unlike a review team wading through thousands of non-responsive documents in search of the few that matter.

The TREC Total Recall track exists precisely because "how much recall is enough" cannot be answered by intuition. It has to be measured, with defined test collections, defined relevance judgments, and reproducible scoring. RAG evaluation is only beginning to build equivalent infrastructure.

Chain of Custody versus Provenance

A litigator asks where a document came from. A historian asks about its provenance. A RAG developer asks which chunk produced a given answer. These are the same question asked by three different professions, and only one of those professions has built a rigorous, court-tested framework for answering it.

Chain of custody in eDiscovery is not paperwork for its own sake. It exists because a document's evidentiary value depends on being able to trace it back to its source without gaps: who collected it, when, from which custodian, and whether it was altered along the way. Knollmeyer and colleagues' review of RAG evaluation dimensions identifies citation quality and context relevance as core axes on which modern systems are judged (Knollmeyer et al., 2026), which is encouraging, but citation quality in most RAG systems today means little more than "a source was attached." It rarely means the source was verified, the retrieval path was logged, or the chunk boundaries were preserved in a way that lets a reviewer reconstruct exactly what the model saw.

This gap matters more as RAG systems move into higher-stakes domains. Zhang and colleagues' comparative study of retrieval strategies in clinical note extraction found that traceability between retrieved evidence and generated output is essential in a domain where an ungrounded claim carries real consequences (Zhang et al., 2026). The parallel to legal discovery is not a stretch. A clinician relying on a RAG system's summary of a patient's history needs the same assurance a litigator needs from a document review platform: an unbroken, inspectable link between the source material and the conclusion drawn from it.

Toward Defensible AI Retrieval

Legal discovery imposes four requirements on retrieval decisions: they must be explainable, reproducible, documented, and defensible under adversarial scrutiny. Those four words are worth sitting with, because RAG evaluation is converging on exactly the same standard, even if the field has not yet named it that way.

Tan and colleagues' work on provably robust aggregation addresses a version of the defensibility problem directly, introducing mathematically grounded methods for making retrieval resistant to corrupted or adversarial evidence (Tan et al., 2026). That is the RAG equivalent of a chain-of-custody challenge in court: what happens when the retrieved evidence itself cannot be trusted, whether through poisoning, duplication, or simple corpus noise? Cho and Lee's redundancy-aware evaluation framework tackles an adjacent problem that will feel familiar to any eDiscovery practitioner: high-similarity corpora, full of near-duplicate documents, distort both retrieval metrics and reviewer judgment if not handled explicitly (Cho and Lee, 2026). Legal collections are famously redundant, forwarded emails, cc'd threads, and near-identical drafts. RAG corpora, especially those built from scraped or aggregated sources, are no different, and the evaluation methods built for one domain transfer cleanly to the other.

I want to propose a working term for what this convergence is pointing toward: Defensible AI Retrieval. A defensible retrieval system is not simply one that performs well on a benchmark. It is one whose outputs can be explained after the fact, whose retrieval logic can be reproduced by a third party, whose decisions are documented well enough to survive scrutiny, and whose failure modes are known rather than discovered by accident. The Sedona Conference's TAR Case Law Primer spends hundreds of pages working through exactly this standard for legal technology, and its core insight, that defensibility is established through process documentation and validation, not through claims of accuracy alone, applies almost without modification to AI retrieval systems (Sedona Conference, 2023).

Quality Assurance: The Discipline RAG Has Not Yet Built

If there is one area where eDiscovery is unambiguously ahead of RAG, it is quality assurance. Legal review workflows are built around statistical validation from the outset: random sampling of the review population, second-level review of a subset of coded documents, dedicated privilege review passes, and formal error correction protocols. None of this is optional or aspirational. It is baked into the workflow because courts require it and because the cost of an undetected error, a privileged document produced by mistake, a responsive document never found, is high enough to justify the overhead.

RAG evaluation has started to build analogous practices, but the culture around them is still immature. Hallucination detection, groundedness scoring, and faithfulness metrics are now common terms in RAG papers. Knollmeyer and colleagues' framework treats faithfulness and context relevance as first-class evaluation dimensions alongside more traditional retrieval accuracy measures (Knollmeyer et al., 2026), and Sotic and Kamps' study of information-seeking behavior in RAG systems adds something the retrieval metrics literature often misses: how users actually interact with retrieved evidence, what they trust, what they miss, and where their mental models diverge from what the system actually did (Sotic and Kamps, 2026). That user-facing lens is closer to what eDiscovery calls second-level review, a human check on whether the system's output actually holds up.

What is still missing from most RAG evaluation pipelines is the statistical rigor that eDiscovery treats as baseline. Cormack and Grossman's 2016 paper on engineering quality and reliability in technology-assisted review argued that retrieval systems should be engineered for measurable quality, not tuned by intuition and then trusted (Cormack and Grossman, 2016). Random sampling with confidence intervals, documented error rates, and formal validation protocols are standard in eDiscovery and still rare in RAG deployment. A scoring rubric borrowed from legal QA might look like this for a RAG system under evaluation: sample a statistically significant subset of generated answers, score each on groundedness (is every claim traceable to a retrieved chunk), faithfulness (does the answer avoid contradicting its sources), completeness (did retrieval surface the material a human reviewer would consider necessary), and false-negative risk (what was missed, and how costly would that omission be in context). Reporting these as point estimates with confidence intervals, rather than as a single aggregate accuracy score, would bring RAG evaluation much closer to the standard eDiscovery already treats as routine.

Ten Lessons RAG Developers Should Borrow

1. Retrieval must be measurable. Intuition is not a validation strategy.

2. Every answer needs provenance. A citation that cannot be traced to its source chunk is not a citation.

3. Retrieval decisions should be explainable, not just accurate.

4. Sampling is mandatory. No system should be trusted on the basis of spot checks alone.

5. Human validation never disappears. Automation reduces review burden; it does not eliminate the need for oversight.

6. Metadata matters. Knowing where a chunk came from, and how it was processed, is as important as its content.

7. False negatives are often more dangerous than false positives. A missed document, or a missed fact, can be more damaging than an irrelevant one.

8. Evaluation should be continuous, not a one-time benchmark run before launch.

9. Retrieval quality should be statistically measured, with error rates and confidence intervals, not asserted.

10. Trust is earned through transparency, not claimed through marketing.

Conclusion

AI did not invent the challenge of trustworthy retrieval. Long before vector databases and large language models existed, courts demanded retrieval systems capable of finding, documenting, defending, and reproducing evidence, and an entire professional discipline grew up around meeting that demand. Retrieval-Augmented Generation (RAG) represents a genuine technological advance. It is not, however, an entirely new discipline, and treating it as one means reinventing quality assurance practices, defensibility standards, and precision-recall tradeoffs that the legal profession has already spent decades refining under adversarial, high-stakes conditions.

The future of trustworthy AI retrieval may depend less on inventing new evaluation methods from scratch than on rediscovering, and adapting, the hard-earned lessons of eDiscovery. The tools will keep changing. The underlying discipline, measurable, provenance-driven, statistically validated, and honest about its failure modes, does not need to be reinvented. It needs to be borrowed.

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Notes

Cho, H., & Lee, J.-Y. (2026). RARE: Redundancy-aware retrieval evaluation framework for high-similarity corpora. ACL 2026. https://aclanthology.org/2026.acl-long.923

Cormack, G. V., & Grossman, M. R. (2015). Autonomy and reliability of continuous active learning for technology-assisted review. https://arxiv.org/abs/1504.06868

Cormack, G. V., & Grossman, M. R. (2016). Engineering quality and reliability in technology-assisted review. Proceedings of SIGIR 2016. https://plg.uwaterloo.ca/~gvcormac/PAPERSx.html

Elkiran, H., & Rasheed, J. (2026). Evaluating retriever reranker pairings in RAG based on quality and efficiency trade-offs. Discover Computing. https://doi.org/10.1007/s10791-026-10156-3

Knollmeyer, S., et al. (2026). Evaluating retrieval augmented generation: A comprehensive review of evaluation dimensions, question types, and application. SN Computer Science. https://link.springer.com/article/10.1007/s42979-026-05134-x

Redgrave LLP (2026). "Putting Gen AI to the Test: A Document Review Accuracy Study. Relativity aiR for Review vs. Active Learning". https://resources.relativity.com/document-review-accuracy-analysis-study-lp.html

The Sedona Conference. (2023). The Sedona Conference TAR case law primer (2nd ed.). https://www.thesedonaconference.org/node/10365

Sotic, B. N., & Kamps, J. (2026). Information seeking behavior in LLM-based RAG: Mental models and missing information. Proceedings of ACM SIGIR 2026. https://doi.org/10.1145/3805712.3808537

Tan, X., et al. (2026). PRA-RAG: Provably robust aggregation in retrieval-augmented generation against retrieval corruption. Findings of ACL 2026. https://aclanthology.org/2026.findings-acl.1794

Wu, S., et al. (2026). Retrieval-augmented generation for natural language processing: A survey. Artificial Intelligence Review. https://link.springer.com/article/10.1007/s10462-026-11605-7

Zhang, H., et al. (2026). Optimising clinical information extraction: A comparative study of retrieval-augmented generation techniques in clinical notes. Journal of Biomedical Informatics. https://www.sciencedirect.com/science/article/pii/S1532046426000778

AI Fact Verification for Historians: Building a Discipline-Specific Protocol for a New Research Environment

by Alvin Blackshear | Historian & Researcher  <ablackshear@gmail.com>

Historians have always worked at the boundary between evidence and interpretation, testing claims against sources, weighing provenance, and living comfortably with uncertainty. Generative artificial intelligence has entered this terrain quickly and unevenly, offering historians an extraordinary tool for discovery while introducing new categories of risk that the discipline has not yet fully reckoned with. The question facing the field is not whether AI belongs in historical research. It already does. The real question is how historians verify what AI produces, and whether existing habits of source criticism are sufficient for a technology that can fabricate footnotes, invent quotations, and drift toward revisionist narratives when prompted to do so.

This article argues that AI can meaningfully accelerate historical research, but it cannot replace professional source criticism, and it proposes the outline of a repeatable verification protocol suited to the particular demands of archival research, primary source authentication, citation validation, chronology checks, and historiographical consistency.

The stakes of getting this right extend beyond any single article or dissertation. History as a discipline depends on a shared confidence that its claims can be traced back to something real, a document, an object, a testimony that survived. When that chain of evidence is compromised, even quietly and even with good intentions, the damage does not stay contained to one footnote. It erodes the credibility of the discipline as a whole, at a moment when public trust in expertise is already under strain. Historians who adopt AI tools without adopting equally rigorous verification habits are not simply risking their own reputations. They are testing the durability of a field that has spent centuries building methods for distinguishing memory from evidence and evidence from invention.

The Promise and the Ceiling

Large language models can summarize secondary literature, suggest starting bibliographies, identify patterns across large text corpora, and help historians locate materials they might otherwise have missed. Used well, these tools compress hours of preliminary searching into minutes. The American Historical Association's Ad Hoc Committee on Artificial Intelligence in History Education, in its 2025 guiding principles, acknowledges this potential while insisting on a firm boundary: generative AI can mimic some of the work historians do, but this should never be mistaken for the work itself. The committee's language is worth sitting with. AI produces texts, images, audio, and video, not truths. It selects words and patterns from training data rather than comprehending the past in its full complexity and contradiction. When a model encounters a gap in its training, it does not pause to acknowledge uncertainty. It fills the gap with something plausible sounding, a process the field now calls hallucination.

For historians, hallucination is not an abstract technical curiosity. It is a direct threat to the evidentiary basis of scholarship. A fabricated citation that looks correctly formatted, attached to a real-sounding archive or journal, can pass a cursory glance and enter a footnote before anyone notices the source does not exist. The AHA's guiding principles are unambiguous on this point: any reference generated by AI must be checked against the original before it is used, and a reference included in a footnote without that check is never acceptable practice, regardless of how the historian frames their working process.

Why Automated Fact-Checking Falls Short

It is tempting to assume that the solution to AI hallucination is simply more AI, in the form of automated fact-checking systems layered on top of generative tools. Recent research complicates that assumption considerably. A 2026 study led by Kelly Amaddio examined how people respond to automated fact-checkers correcting misinformation, using a sample of more than thirteen hundred participants exposed to a piece of gun control misinformation and a subsequent AI-generated correction. The findings revealed a persistent and troubling pattern: prior beliefs shaped both continued endorsement of the misinformation after correction and perceptions of the fact-checking system's reliability. When the fact-checker was described as only moderately accurate, close to the accuracy levels people already associate with existing AI systems, prior beliefs predicted whether the correction actually changed minds. Even when participants were told the system was highly accurate, the effect of prior belief did not disappear, only lessened.

This matters for historians because it demonstrates that automated verification does not operate in a neutral space. It is filtered through the same human biases that shape how any evidence is received, and it can be defeated by claims that already align with what someone wants to believe. Historical scholarship, particularly on contested and politically charged topics, is especially vulnerable to this dynamic. A fact-checking layer bolted onto a language model cannot substitute for the disciplinary training that teaches historians to interrogate their own assumptions as rigorously as they interrogate a source.

Revisionism as a Distinct Problem

Contested historical claims present a challenge that generic fact-checking frameworks were never designed to solve, because the disagreement is not always about a discrete, verifiable fact but about interpretation, framing, and the political stakes attached to memory. A 2026 study by Francesco Ortu and collaborators addresses this directly. The researchers built a benchmark called Historical Misinfo, drawing on five hundred contested historical events from forty five countries, each paired with a documented factual narrative and a documented revisionist counter-narrative. They then tested how large language models responded under neutral prompting compared with prompting that explicitly requested the revisionist version of events.

The results are instructive. Under neutral conditions, most models leaned closer to the factual reference. But when users explicitly requested a revisionist framing, every model tested showed a sharp increase in revisionist output, with little resistance or self-correction. This finding should give historians real pause. It suggests that a model's apparent reliability on a given topic says little about how that same model will behave when a user, deliberately or not, nudges it toward a distorted account. For historians working on genocide, colonialism, contested territorial claims, or any subject where competing national narratives exist, this is not a marginal concern. It is central to responsible use.

What Verification-First Research Looks Like Elsewhere

Historians are not alone in confronting this problem. Related work emerging from adjacent fields offers useful models for how disciplines might restructure their relationship with AI around verification rather than convenience. Lei You, Lele Cao, and Iryna Gurevych have argued that AI-assisted peer review in the sciences should be verification-first rather than review-mimicking, proposing that AI tools function as adversarial auditors generating checkable evidence rather than as systems that simply predict what a human reviewer might conclude. Belinda Mo extends this argument to the broader landscape of autonomous AI research agents, warning of a widening verification gap between what AI systems can produce and what any human can realistically check, and calling for verification infrastructure built around observable workflows and clear attribution.

A study by Raymond Solga and Mohammed Sarwar offers perhaps the closest parallel to the historian's own situation. Comparing traditional historical methods against several generative AI tools in identifying and validating primary sources in ancient history, the researchers found that AI performed well at broad content discovery and thematic synthesis but struggled significantly with genre boundaries, provenance transparency, and the kind of contextual interpretation that trained historians bring almost instinctively to a source. Their proposed solution, a human-in-the-loop framework built around model pluralism and provenance-first protocols, maps closely onto what historians already know from decades of archival practice, simply extended to include AI as one input among many that must be checked rather than trusted.

There is also a temporal dimension worth naming. Carlo Iacono's recent audit of AI-assisted research output found that the models cited in published work are frequently outdated by the time readers encounter them, sometimes by close to a year, with model versions superseded and behaviors shifting in ways that are rarely documented. For historians, this is a reminder that any claim about what a given AI tool can or cannot do should be dated and treated as provisional rather than as a permanent property of the technology.

Toward a Repeatable Protocol

Drawing these threads together, a workable verification protocol for historians should rest on several commitments. Every AI-generated citation must be checked against the primary source or an authoritative secondary source before it appears in any written work, with no exceptions for citations that merely look plausible. Chronology and provenance claims generated by AI require independent confirmation, since these are precisely the areas where current tools show the weakest performance. Contested or politically charged claims deserve heightened scrutiny, given the demonstrated tendency of models to drift toward whatever framing a prompt implies. Verification work should be documented as it happens, noting which tool was used, when, and what was checked, both for the historian's own accountability and because the tools themselves change quickly enough that undocumented claims about their reliability age poorly. And throughout, the historian's own expertise must precede rather than follow AI use, since evaluating a model's output well requires the same depth of subject knowledge that historians have always needed to evaluate any source.

None of this diminishes the real value AI offers historians willing to use it carefully. It does insist that the discipline's oldest habit, treating every claim as something to be verified rather than assumed, remains the right response to a genuinely new technology. Generative AI can accelerate the search for evidence. It cannot substitute for the judgment that turns evidence into history.

There is a version of this moment that history has lived through before, in smaller ways, each time a new technology promised to change how the past is studied. Microfilm, digitization, keyword-searchable databases, all reshaped the pace and reach of archival work without ever touching the core discipline of asking what a source can and cannot tell us. Generative AI is different in degree, not necessarily in kind. It compresses years of searching into minutes, but it also compresses the distance between plausible and true, and that compression is precisely where a historian's training earns its keep. The AHA's committee put it plainly: there are no shortcuts to expertise, and the skills that let a scholar recognize a hallucinated citation or a subtly revisionist framing can only be built through the same sustained engagement with sources that has always defined the craft. A historian who verifies rigorously will find in AI a genuine research partner. A historian who does not will find, sooner or later, that the past they have written no longer holds.

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Sources

American Historical Association, Ad Hoc Committee on Artificial Intelligence in History Education. "Guiding Principles for Artificial Intelligence in History Education." Approved by AHA Council, July 29, 2025. https://www.historians.org/resource/guiding-principles-for-artificial-intelligence-in-history-education/

Amaddio, Kelly M., et al. "Prior Beliefs and Automated Fact Checking: Limits on the Accuracy of AI Verification." February 5, 2026. https://doi.org/10.1371/journal.pone.0342332

Iacono, Carlo. "The Half-Lives of Generative-AI Evidence." July 27, 2026. https://doi.org/10.48550/arXiv.2607.24032

Mo, Belinda. "The Age of AI Agents Demands a New Scientific Paradigm to Sustain Trustworthy Science." June 18, 2026. https://doi.org/10.48550/arXiv.2607.26064

Ortu, Francesco, et al. "Preserving Historical Truth: Detecting Historical Revisionism in Large Language Models." February 22, 2026. https://doi.org/10.48550/arXiv.2602.17433

Solga, Raymond S., and Mohammed J. Sarwar. "Evaluating Generative AI in Historical Research." AI & Antiquity (2026). https://doi.org/10.64946/aiantiquity.v2i1.003

You, Lei, Lele Cao, and Iryna Gurevych. "Preventing the Collapse of Peer Review Requires Verification-First AI." February 12, 2026. https://doi.org/10.48550/arXiv.2601.16909