AI / KNOWLEDGE / GOVERNANCE / INTELLIGENCE
Why RAG Fails Law Firms.
Many firms find that, in practice, Retrieval-Augmented Generation (RAG) rarely delivers the consistency, confidence, or reliability they were promised.
The problem is not RAG itself.
It is the assumption that a single retrieval layer can compensate for deeper structural issues in how legal knowledge is created, stored, governed, and reused.
Legal knowledge is inherently complex:
- fragmented across systems
- written for different purposes and audiences
- governed by ethical walls
- and updated unevenly over time
When this underlying reality is ignored, RAG systems tend to surface plausible but partial answers - responses that sound correct, yet lack the context, authority, or completeness required for legal work.
In these conditions, hallucination risk is reduced, but not eliminated; inconsistency simply moves upstream.
A reliable RAG strategy begins well before retrieval.
It requires disciplined preparation of the knowledge base itself, informed by repeated deployments across real legal environments: normalising content, respecting document structure, aligning terminology across practice groups, and ensuring material reflects current, authoritative positions.
The ability to control context is critical, filtering aggressively, reducing noise, and enforcing governance through architecture, not policy alone so that models are constrained to what is known, permitted, and verifiable. Without this, even advanced RAG architectures invite improvisation around gaps in the source material.
When implemented correctly, RAG is not a standalone solution but a core component of a broader legal intelligence strategy. Done well, it reduces manual research effort, preserves legal accuracy, respects ethical boundaries, and makes firm-wide expertise accessible in seconds, without sacrificing trust or defensibility.
Join
Zsolt Apponyi
and
Illitch Real
of
Rubiklab
as they explore real-world success and failure modes of legal RAG, and share practical, experience-led guidance on how to design, govern, and operationalise RAG the right way.
Key Details
DATE: Friday, January 23rd, 2026
TIME: 10:00am-11:00pm US Central / 4:00pm - 5:00pm GMT
LOCATION: Online - Inside Practice Community
Key Themes & Discussions
Why RAG Fails in Practice
Explore the common pitfalls law firms encounter when adopting RAG, from assuming retrieval alone fixes underlying knowledge fragmentation to overlooking governance, context control, and content quality.
Preparing Knowledge for Reliable AI
Understand how disciplined knowledge preparation including structuring, normalisation, semantic alignment, and document hygiene is the foundation for dependable RAG.
Governance, Context, and Control
Discover how firms can enforce governance, ethical walls, and context filtering so RAG systems generate responses that are defensible, relevant, and compliant.
Building RAG That Scales
Insight into practical frameworks for integrating RAG into firm workflows, balancing recall with precision, and transitioning from experimental pilots to reliable, enterprise-ready capabilities.
From Retrieval to Reliability:
Making RAG Work in Law Firms
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your place by 01.16.25 code
RAGAI
Who Should Attend
This webinar is designed for legal professionals responsible for evaluating, implementing, or governing Retrieval-Augmented Generation (RAG) and data-connected AI systems within their organizations, including:
- Law firm partners, practice group leaders, and firm leadership seeking to understand how RAG can be deployed safely, effectively, and at scale without increasing operational or risk exposure.
- Chief Knowledge Officers, Knowledge Management leaders, and legal innovation teams responsible for improving access to institutional knowledge, precedents, and firm intelligence through AI-enabled systems.
- Legal technology, data, and IT leaders involved in AI architecture, information governance, data quality, and system integration.
- In-house legal operations and legal technology leaders exploring RAG to support internal legal teams with faster, more reliable access to trusted information.
This session will be especially valuable for those navigating the tension between AI experimentation and the need for control, accuracy, security, and trust in legal environments.
Our Speakers
Illitch Real
CEO & Co-Founder
Rubiklab
Illitch Real is co founder of Rubiklab, where he focuses on designing and delivering knowledge systems for professional services firms. He has over twenty years of experience working with complex, high risk data environments across law, consulting, healthcare and research organisations. His work centres on turning fragmented internal knowledge into governed, usable intelligence through rigorous data preparation, quality measurement frameworks and defensible AI architectures. Illitch has worked with both top tier and mid sized firms on large scale deployments, helping them move beyond experimental AI towards systems that can be trusted in day to day professional decision making.
Zsolt Apponyi
Chief Technology Officer
Rubiklab
Zsolt Apponyi is co founder and CTO of Rubiklab, where he leads the engineering of large scale AI and knowledge systems for professional services firms. His background is in designing end to end architectures that sit behind production grade RAG, search and knowledge intelligence platforms. Zsolt specialises in data ingestion pipelines, document structuring, semantic indexing, access control, and context management across complex, permissioned environments. He has built and deployed systems operating over millions of documents, integrating multiple internal and external sources while enforcing traceability, auditability and strict governance. His work focuses on making AI reliable in practice, reducing hallucination risk through engineering discipline, quality protocols and architectural constraints rather than prompt level fixes.
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