RAG vs Fine-Tuning: Which Do Law Firms Actually Need?

Vendors love to talk about “RAG” and “fine‑tuning”. For many partners, these sound like magic acronyms rather than practical choices.

In reality, they are just two different ways of getting AI to work with your firm’s knowledge and documents. Understanding the difference helps you:

ask better questions of suppliers;
avoid paying for complexity you do not need; and
choose a path that fits your size and risk appetite.

This article explains RAG and fine‑tuning in plain English and sets out when each makes sense for law firms.

What is RAG?

Retrieval‑augmented generation (RAG) is a pattern where:

The system takes your question.
It searches a document store or database for relevant material.
It feeds the most relevant passages, plus your question, into a language model.
The model generates an answer that is grounded in those passages.

Key features:

Your documents stay in a searchable index rather than being mixed into the model itself.
You can usually see which passages were used to generate the answer (citations or “source cards”).
Updating the knowledge base is as simple as adding or removing documents.

For most legal research and knowledge tasks, this is exactly what you want: the model acts like a very fast reader of your own materials.

What is fine‑tuning?

Fine‑tuning means taking an existing base model and training it further on a curated dataset to nudge its behaviour in a particular direction.

Examples outside law include:

training a model to write in the style of a particular brand;
improving performance on a specific technical domain;
teaching it to follow complex instructions more reliably.

In fine‑tuning:

knowledge is baked into the model’s parameters;
the boundary between “training data” and “normal inputs” is blurred; and
updating what the model “knows” often means another training round.

This can be powerful, but it is more complex to build, govern and explain.

Which do law firms actually need?

For most firms, RAG will do the heavy lifting:

You keep client and knowledge documents in systems you already trust (DMS, case management, knowledge bases).
AI is used to search, rank and summarise those documents.
You can show fee‑earners (and regulators) exactly which sources were relied on.

Fine‑tuning may be appropriate when:

you need a very consistent drafting style across thousands of similar outputs;
you have a large, high‑quality set of example inputs/outputs that you own and can use for training; and
you are comfortable with the governance implications (for example, how training data is selected, reviewed and updated).

In other words: start with RAG unless there is a clear, well‑justified reason not to.

Practical examples

RAG‑style scenarios

Searching your own advices and know‑how notes for prior work on a point.
Asking, “Show me the three clauses in our precedents that deal with X, with pros and cons for each.”
Generating a first‑pass summary of a large disclosure set, with links back to underlying documents.

Fine‑tuning scenarios

A volume practice (for example, small claims or commoditised contracts) where you want models to produce drafts that closely match historic work.
Internal tools where you have already standardised templates and precedents, and you want the model to “think like your firm”.

Even in these cases, fine‑tuning is usually combined with RAG so that up‑to‑date documents are still retrieved and cited.

Governance considerations

Whichever route you take, remember:

Data protection and confidentiality – if you use client documents for RAG or fine‑tuning, ensure you have a lawful basis and appropriate safeguards.
Auditability – regulators and clients may want to know how a particular output was produced. RAG’s explicit citations often make this easier.
Change management – who decides when to add or remove documents, or when to re‑train a fine‑tuned model?

Many firms start with vendor platforms that hide some of this complexity but you should still ask questions until you are comfortable.

Where OrdoLux fits

OrdoLux is being designed around a RAG‑first approach:

matter documents and knowledge materials stay inside your existing systems;
AI helps retrieve and summarise them with clear links back to sources; and
you retain control over which materials are in scope for each matter or tool.

In the longer term, where fine‑tuning makes sense for specific, high‑volume workflows, it can be layered on top of this foundation – but never as a black box.

This article is general information for practitioners — not legal advice.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined‑up system. Learn more on the OrdoLux website.