4 Retrieval-Augmented Generation
While large language models (LLMs) demonstrate impressive capabilities in generating human-like text and reasoning through complex problems, they face fundamental limitations when applied to legal practice. These models are trained on vast datasets with specific knowledge cutoff dates, meaning they cannot access information published after their training concluded. More critically for legal practitioners, LLMs lack access to internal documents, such as client files, internal precedents, or proprietary knowledge bases that often contain the most relevant information for practical legal work.
Consider a practical scenario. You are researching recent developments in Australian privacy law following amendments to the Privacy Act. A standard LLM might provide general information about privacy principles. Still, it cannot access the latest privacy determinations from the Office of the Australian Information Commissioner, recent Federal Court decisions, or internal analysis of how these changes affect your clients. This limitation significantly constrains the practical utility of LLMs for many legal applications.
What is Retrieval-Augmented Generation?
Watch
Watch this video from IBM Senior Research Scientist Marina Danilevsky, who explains what RAG is and the two significant advantages that combining RAG with LLMs can deliver on.
Retrieval-Augmented Generation (RAG) addresses these limitations by combining the generative capabilities of LLMs with dynamic information retrieval. Think of RAG as giving an AI system access to a specialised reference library that it can consult in real-time while formulating responses. The RAG process works through three distinct phases:
1. Retrieval Phase: When you submit a query, the system searches through specified knowledge bases to identify relevant information, including legal databases, case law repositories, internal firm documents, and client files.
2. Augmentation Phase: The system takes the most relevant retrieved information and incorporates it into the context provided to the LLM, essentially “briefing” the model with pertinent background material.
3. Generation Phase: Using both its pre-trained knowledge and the retrieved information, the LLM generates a response that is grounded in authoritative, up-to-date, and contextually relevant sources.
This architecture transforms AI from a system that relies solely on historical training data into one that can work with current, specific, and authoritative information sources.
How RAG Differs from Standard Interactions
The difference between standard LLM interactions and RAG-enhanced systems can be presented in the flowcharts below:
Standard LLM Approach:
Your Query → LLM (relying only on training data) → Response
RAG Approach:
Your Query → Search Knowledge Base → Retrieve Relevant Documents → LLM (training data + retrieved information) → Response
RAG in Legal Practice
1. Case Law Research and Analysis
RAG systems can maintain current databases of judicial decisions, automatically incorporating new judgments as they’re published. When you query recent developments in a specific area of law, the system retrieves the most current authorities and provides analysis based on the authoritative sources.
Example Application: “What’s the current approach to assessing deceptive similarity under the Trade Marks Act 1995 (Cth)?”
A RAG system with access to recent decisions from the High Court, Federal Court, Federal Circuits and Family Court, and the Australian Trade Mark Office can provide analysis based on the latest decisions, incorporating recent trends in judicial reasoning that would not be reflected in an LLM’s training data.
2. Internal Knowledge Management
Legal practices accumulate substantial institutional knowledge through precedent documents, internal memoranda and training materials. RAG systems can make this knowledge searchable and actionable.
Example Application: “How did we successfully defend the last data breach notification claim?”
The system retrieves relevant internal files, strategy documents, and correspondence, enabling practitioners to build on established firm knowledge rather than starting from scratch.
3. Regulatory Compliance and Updates
RAG systems can maintain current databases of regulatory instruments, guidance notes, and compliance materials, ensuring advice reflects the latest regulatory position.
Example Application: “What are the current ASIC requirements for related party transaction disclosure?”
The system accesses current ASIC regulatory guides, recent determinations, and compliance materials to provide current guidance.
4. Client-Specific Document Analysis
RAG enables AI systems to work with client-specific document sets, maintaining confidentiality while providing sophisticated analysis capabilities.
Example Application: During due diligence, upload a client’s complete contract portfolio and ask: “Identify all change of control provisions and assess their implications for the proposed transaction.”
The integration of RAG into legal practice represents a significant advancement on LLMs. By understanding the capabilities of these systems and implementing appropriate quality controls, legal practitioners can harness RAG to utilise LLMs with more comprehensive, current, and accurate legal information.
