RAG vs. Private LLMs: Making the Right Choice for Your Legal Organization
By John Tredennick
As legal organizations race to embrace artificial intelligence, there is an important discussion that needs to take place about the two core deployment options: private Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. The issues are focused on implementation and maintenance, security, customization, and costs–vital factors that can either make AI deployment an immediate success or a prolonged challenge. Let’s get to the heart of the matter while outlining what each of these models actually do.
The Appeal of a Private AI System
Interest in private LLMs stems from compelling strategic considerations. Training an AI model on decades of carefully crafted legal documents, innovative solutions, and hard-won insights presents an attractive vision of institutional knowledge preservation and leverage. This appeal is rooted in several key factors that resonate deeply with law firm leadership.
First, private LLMs promise control over training data. Firms can theoretically curate their training sets to include only their highest-quality work product, ensuring the model reflects their best thinking and preferred approaches. This level of control extends to writing style, analytical frameworks, and firm-specific procedures that distinguish the firm’s work in the marketplace.
Second, the customization potential of private LLMs suggests the ability to embed firm-specific expertise and methodologies directly into the AI system. This could include specialized knowledge in niche practice areas, proprietary deal structures, or unique litigation strategies that represent significant intellectual capital.
Third, security considerations would seem to weigh heavily in favor of private LLMs. The ability to maintain complete control over sensitive client information and proprietary work product within the firm’s own infrastructure appears to offer the highest level of data protection and confidentiality compliance. But more on that later.
Challenges to Consider
With these three benefits in mind, the reality of implementing and maintaining private LLMs presents a far more complex picture. These challenges manifest in several critical dimensions that firms must carefully consider.
- Initial Investment: Implementation requires substantial capital investment, typically in the six-figure range, covering data preparation, model development, infrastructure, and specialized AI personnel.
- Operational Costs: Running a private LLM demands significant computing resources. Annual infrastructure and staffing costs can reach millions of dollars, far exceeding traditional legal technology expenses.
- Updating Problems: Private LLMs cannot be simply updated with new content. They require complete retraining to incorporate new developments or work product, creating significant operational overhead.
- Performance Gap: Private LLMs typically achieve performance levels comparable to GPT-3.5, while commercial models like GPT-4 and Claude 3.5 continue advancing. This creates an expanding capability gap that becomes increasingly expensive to bridge.
To be sure, the newest open source models from DeepSeek claim equal or superior capabilities to models like GPT 4o and Claude Sonnet 3.5 but there are obvious concerns with the Chinese models. Just remember that once you lock in, you will stay locked in until you can update the model.
At the least, make sure you investigate and are comfortable with these issues.
The RAG System Alternative
Although clumsily named, Retrieval-Augmented Generation (RAG) systems present a sophisticated, yet practical, alternative that addresses an organization’s core knowledge management needs while avoiding the substantial drawbacks of private LLMs. By combining powerful commercial LLMs with intelligent document retrieval, RAG systems deliver immediate value while maintaining flexibility for future advancement.
At its core, a RAG system operates through a three-component architecture that elegantly solves the challenge of keeping AI responses current and accurate. The system begins with a sophisticated search platform that indexes and maintains the firm’s document repository.
When a user poses a question, this platform identifies the most relevant documents from the firm’s collection, including the most recent work product. These documents are then presented to a state-of-the-art, commercial LLM along with the user’s query, enabling the AI to generate responses grounded in the firm’s actual work product and expertise.
This architecture offers several distinct advantages over private LLMs. First, document currency is maintained through routine indexing rather than expensive model retraining. New work product becomes available for AI analysis as soon as it’s added to the system, typically through automated nightly updates. This ensures that responses always reflect the firm’s latest thinking and developments in the law.
Second, RAG systems leverage the most advanced commercial LLMs available, such as GPT-4o and Claude 3.5, providing significantly superior analytical capabilities compared to private models. As these commercial models continue to evolve and improve, RAG systems automatically benefit from these advancements without additional investment or technical overhead.
Third, the implementation timeline and resource requirements for RAG systems are dramatically more favorable. While private LLMs require months of development and training, RAG systems can be deployed in days or weeks, providing immediate value to the firm. The technical expertise required for operation is also substantially lower, focusing on system configuration rather than complex model development and maintenance.
Security and Control in RAG Systems
While security concerns often drive people toward private LLMs, modern RAG systems implement multi-layered security that matches any private system, including:
- Isolated Architecture RAG security begins with single-tenant architecture, where each firm operates in isolation within a dedicated cloud environment under their control. This ensures complete data separation while maintaining cloud infrastructure benefits.
- Access Control and Encryption Firms retain ownership of encryption keys and implement granular controls matching their organizational structure, enabling precise management of information access and security.
- Geographic Data Control Deployment options enable compliance with data sovereignty requirements and regional privacy regulations like GDPR and CCPA through controlled data storage and processing locations.
- Comprehensive Monitoring Audit trails and system monitoring provide complete visibility, with all interactions logged and analyzable to support ethical compliance obligations.
Private LLMs require a lot of computing horsepower, which means you will be running these in the cloud, just like a RAG system. From that standpoint, security is a push, simply depending on the level of expertise of the team managing the infrastructure.
What about Enterprise LLM Security?
Many firms initially questioned the security of public LLMs like GPT and Claude, particularly after ChatGPT’s public beta reserved rights to use submitted data for training. However, enterprise LLM implementations now provide comprehensive security through contractual and architectural safeguards.
Major providers like Microsoft, OpenAI, and Anthropic offer enterprise agreements with specific legal data protections:
- All communications remain private and temporary
- No data retention or model training use
- Information exists only in the momentary context window
- Complete isolation from other users’ data
Perhaps the most important thing to understand is this: LLMs can’t remember or share the data you send to them, regardless of whether they are public or private. To the contrary, the data you send for analysis typically stays with the LLM for about the time it takes the LLM to respond. Further requests to the LLM must include the previous information in your conversation.
For comparison, consider the email and other files organizations regularly store with a cloud provider such as Microsoft or Google. That data, which includes client and organization confidential information, can reside on cloud servers for months or years. As a practical matter, the milliseconds of context window exposure with an LLM present significantly less risk than conventional document management systems.
Implementation and Integration
The deployment timeline for a RAG system stands in stark contrast to private LLM implementation. While private LLMs typically require 6-12 months of development before they can be used, RAG systems can begin delivering results within days or weeks. They can also be deployed gradually with specific practice groups or use cases, allowing for controlled rollout and iterative improvement based on user feedback.
Ultimately, the key advantage is that RAG systems don’t require specialized AI expertise to implement. And, with utility-based pricing, organizations can match costs to actual use.
About the Author
John Tredennick (jt@merlin.tech) is the CEO and Founder of Merlin Search Technologies, a software company leveraging generative AI and cloud technologies to make investigation and discovery workflow faster, easier, and less expensive. Prior to founding Merlin, Tredennick had a distinguished career as a trial lawyer and litigation partner at a national law firm.
With his expertise in legal technology, he founded Catalyst in 2000, an international ediscovery technology company that was acquired in 2019 by a large public company. Tredennick regularly speaks and writes on legal technology and AI topics, and has authored eight books and dozens of articles. He has also served as Chair of the ABA’s Law Practice Management Section.
You Will Need a RAG System Anyway
Ironically, even if you decide to invest in a private LLM, you’ll still need to add a RAG system. This necessity stems from a fundamental limitation of language models. Once trained, they exist in a static state and cannot incorporate new information without complete retraining. Every document created after the training cutoff—from new client matters and court decisions to internal memoranda and strategic analyses—remains invisible to your private LLM until the next retraining cycle.
Given the substantial costs and technical complexity of retraining, most organizations find monthly or even quarterly updates impractical. A RAG system, by contrast, can index new documents as they’re created, making them immediately available for search and analysis through your AI interface.
One day they may invent private models that can be updated nightly, but that day is not today.
Making AI Integration Work for Legal Organizations
As legal organizations integrate AI into their practice, the choice between private LLMs and RAG systems represents a pivotal strategic decision that will shape their technological trajectory for years to come. Modern RAG systems combined with commercial LLMs like GPT-4 or Claude deliver not just superior analytical capabilities, but a sustainable path to AI adoption. They offer the security and control legal organizations require while solving the fundamental challenge of keeping knowledge current and accessible. For most firms, this practical and cost-effective approach eliminates the complexity of managing multiple AI systems while providing immediate value to their practice.