How Retrieval-Augmented Generation Makes AI Smarter with Personalized Law Firm Data

Imagine you're BENJAMIN who is just hired by a law firm PEARSON HARDMAN. Their goal? To integrate AI into their internal systems so that legal documents, case files, and client information can be retrieved effortlessly - just by asking a question. I know it sounds like Harvey, but they just want to reduce human error, eliminate confusion, and minimize manual effort to get them through their pro-bonos.

Sounds like a perfect job for a Large Language Model (LLM), right?

Well… it comes with different challenges. (No, not LOUIS LITT challenge!)

The Problem with Using LLMs in Legal Systems

When you integrate an LLM like GPT into a legal system, two major issues arise:

1. LLMs don’t know your firm dataset or any legal documents
   These models are trained on public datasets, not your firm’s private case files. So, if you ask about “Case 425,” the model might hallucinate or give vague answers.

2. Exact document matching is unreliable and vague
   Even if a document exists in the system, the LLM won’t know unless it’s explicitly given that content. You can’t expect it to magically recall a word-for-word match.

Donna: So, what’s next?
Benjamin: One might think let’s give dataset to LLM and see how it responds. Let’s deep dive into the approach.

The Naive Approach: Stuff Everything into the Prompt

Let’s say you have 3000 documents. You could try putting all of them into the system prompt and then ask the LLM:

User Query: “Which of these documents are similar to Case 425?”

This works… until it doesn’t.

Why This Doesn’t Work

• You can’t fit 3,000 documents into a prompt. There’s a token limit.

• For Case 425, maybe only 10 documents are relevant. Including the rest is wasteful.

• Every time you run this, you’re paying for compute and time to filter documents manually, making LLM compromise with performance and efficiency.

Donna: So how do we make this smarter? Also make it easy for our readers to understand.

Step 1: Pre-filter Relevant Documents

One of the approaches could be that - Instead of asking the LLM to filter documents every time, we can do this upfront:

for (let doc of documents) {
    if (isRelated(doc, userQuery)) {
        relevantDocs.push(doc);
    }
}

That means we filter doc related to the data and categories it as relevant data for the specific query. Now we only pass the relevant documents to the LLM. This reduces cost and improves accuracy and performance drastically.

One might think next that how do we know which documents are relevant? For that we can use Semantic and context of data.

Step 2: Use Semantic Meaning with Vector Embeddings

Every document has meaning - not just keywords, but context as well. We can capture this using vector embeddings.

Here’s how we will approach with embeddings:

• Break each document into chunks (e.g., page by page).

• For each chunk, generate a vector embedding using a model like OpenAI’s embedding API.

• Store these vectors in a vector database.

This way each chuck is mapped to its vector embedding and gets stored in Vector DB. This process of chunking and embedding is called Indexing.

Extra Note to Readers:

If you are new to Vector DB, here are some popular vector DB with their characteristics: -

Step 3: Retrieval + Generation = RAG

After Indexing, now let’s say Harvey asks Mike a query:

“Can you tell me about Case 425?”

Now based on indexing data, chunks and vector embedding stored in DB, here’s what happens:

1. We embed the Harvey’s query into a vector.

2. Search the vector database for similar document chunks.

3. We Retrieve the top relevant chunks.

4. Now again pass those chunks + query to the LLM as input.

5. Hence a personalized, accurate answer is generated.

This entire system is called Retrieval-Augmented Generation (RAG). This two-step pipeline separates retrieval from generation, ensuring responses are both accurate and efficient. And using this, Mike comes to Harvey’s rescue very quickly!

Understanding Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation, or RAG, combines the best of both worlds:

• Retrieval: Searches your own data store to find facts and context.

• Generation: Leverages an LLM to craft human-quality responses based on the retrieved information.

This makes it stand out in the field of AI.

Why RAG Is a Game-Changer

RAG bridges the gap between static LLMs and dynamic, real-world data. It allows AI to:

• Answer questions based on your private documents

• Avoid hallucinations by grounding responses in retrieved facts

• Scale efficiently by indexing once and retrieving fast

In short, RAG makes AI smarter, safer, and more useful-especially in domains that need customized output in their respective fields.

Conclusion (by BENJAMIN)

Retrieval-Augmented Generation through the above example highlights its capabilities to transform how legal professionals can collaborate with artificial intelligence for better output (and avoid getting scolded by LOUIS). By separating document indexing from generation, RAG ensures that responses are both accurate and contextually grounded and they gracefully scale as your document repository grows. Its relevance and significance do not just prove its potential in the field of law, but it can bring innovation in the domains like healthcare, and enterprise systems and offer them better future.

DONNA: Good work Benji, GOOD WORK!!