What Is Retrieval-Augmented Generation (RAG)? — Overcoming the Limitations of Fine-Tuning & Vector-Only RAG


Large language models (LLMs) like GPT are undoubtedly powerful, yet their capabilities are often limited as a standalone, pre-trained tool. LLMs need to be supported with organizational and domain-specific knowledge for accurate, explainable, and context-aware responses.

Retrieval-augmented generation (RAG) addresses these fundamental challenges by connecting the LLM to an external data source of truth.

In this blog post, we’ll explore RAG, how it works, and when you should implement it. You’ll also learn how to enhance RAG capabilities even further with vector search and knowledge graphs.

What is retrieval-augmented generation (RAG)?


Retrieval-augmented generation (RAG) is a technique that enhances the accuracy and reliability of large language models (LLMs) by backing them with facts from external data stores.


Why RAG is Crucial for Enterprise-Grade LLM Applications

Most LLMs are pre-trained on massive public datasets. This type of pre-trained LLM does an impressive job for general-purpose uses. However, LLMs often perform poorly for many business applications due to the following problems:

    • AI hallucination: LLMs often rely on self-supervised training models and, as a result, often struggle with domain-specific tasks that require specialized knowledge and high accuracy thresholds. In turn, LLMs may provide incorrect, nonsensical, or completely fabricated answers. This phenomenon, called “hallucination,” could lead to flawed conclusions and poorly informed business decisions.
    • Lack of context: More often than not, LLM training is not heavily weighted on data specific to your domain, business, or industry. So, if you ask a specific question, the LLM may come up with irrelevant, ambiguous, or otherwise incomplete responses taken out of context, which may not be particularly useful to your specific use case.
    • Lack of explainability: Stand-alone, pre-trained LLMs cannot easily verify, trace, or explain how responses are derived. Sometimes, they may even invent a source. This damages the trust and adoption of that application.
    • Static data: Unless you continuously release re-trained models, a pre-trained LLM will become outdated as time goes on, leading to no answer or, worse, hallucination based on obsolete data.

When planning to use LLMs to build business applications, enterprises should tailor the LLM to their use case by adding another layer of data with retrieval augmented generation.


How RAG Improves Response Accuracy for User Trust

RAG retrieves knowledge from external data stores, such as an organization’s proprietary domain-specific data, and incorporates that knowledge into the LLM in real time. This data helps fill in the gaps in the LLM’s knowledge, resulting in more relevant, accurate, specific, and reliable responses tailored to your use case. Here are some benefits of RAG:

    • Up-to-date information: RAG feeds the LLM with the most recent data in your database. As long as you keep your database updated with the latest data, the LLM can retrieve that knowledge and provide you with up-to-date information.
    • Increased accuracy: RAG provides the LLM with a source of truth, increasing the accuracy and reliability of responses while reducing hallucination risk.
    • Depth and context: Your database can store all the data specific to your business or industry. This way, the LLM can match the prompts with relevant information from the database to provide more specific and in-depth responses.
    • Enhanced user trust: With RAG, the LLM can cite the origin of the information they’re giving you. They can verify, trace, and explain where, why, and how they chose the information they did. They add confidence and transparency to the responses, increasing user trust.

The RAG Architecture: How Does It Work?

At its core, RAG is about connecting LLM’s internal knowledge with external sources of knowledge. To understand how this process works, we need to take a look at the RAG architecture.

The retrieval-augmented generation process

1. Prompt

A user inputs a prompt. This is the first interaction where a GenAI application can accept and process the user query.

2. Information Retrieval

Based on the prompt, the GenAI application can leverage LLMs and/or embedding models to conduct a smart search against the external database based on the user query.

This is often done using vector similarity search, which converts queries into numerical representations and matches them with similar content in the external database. The relevant information is then retrieved from the external database and sent back into the GenAI application, where it combines with the original prompt and sent to the LLM.

3. Response Generation

The RAG-powered LLM, with the newfound information retrieved from the database, generates a comprehensive response. Finally, the generated response, with relevant information backed by the external database, is delivered to the user.

Robot reading up on new knowledge from a library by DALL-E


RAG Use Cases

RAG-powered LLM applications unlock a wide range of real-world use cases. They are especially crucial in fields that require a deep understanding of vast amounts of data and context. Here are some of the most notable use cases of RAG:

    • Company Chatbots: A RAG chatbot provides more accurate, detailed, and context-aware answers to your customer inquiries by pulling from an up-to-date knowledge base specific to your business.
    • Medical Diagnosis Assistance: In healthcare, RAG can assist doctors by retrieving relevant patient data, medical literature, and the latest research to suggest potential diagnoses or treatments.
    • Legal and Compliance: Lawyers and legal researchers can use RAG to sift through large databases of legal documents and regulations to find relevant case information and legal precedents.
    • Content Creation and Recommendation: RAG provides content creators with relevant information and facts to enrich their content. It also suggests relevant articles, videos, or music to users based on their past interactions and current trends by retrieving data from a content database.
    • Interactive Storytelling and Gaming: In gaming and interactive media, RAG can generate narrative content dynamically by retrieving story elements, character information, and dialogue options based on user choices.
    • Research and Development: RAG aids researchers by pulling data from scientific databases and journals to answer complex research queries or to contribute to literature reviews.

Fine-Tuning vs. RAG

Fine-tuning is another popular technique used to improve LLM responses that are context-specific to a certain set of data. Unlike RAG, which retrieves data from external data sources, fine-tuning further trains a pre-trained LLM on a specific set of data to perform specific tasks.

The fine-tuning process

Fine-tuning works best when you have a static set of domain-specific training data labeled correctly. It grants specialization over a specific domain and granular control over the LLM’s response to specific queries.

However, fine-tuning has several downsides:

    • Fine-tuning is much more time-consuming and difficult to implement than RAG.
    • Fine-tuning is not efficient when the data needs to be updated very often, whereas RAG is updated near real-time as the database is updated.
    • Fine-tuning doesn’t solve LLM explainability issues. You still don’t know where the information came from.
    • Fine-tuning can’t personalize responses for unique users.
    • It’s almost impossible to enforce role and access controls with fine-tuning, as opposed to database security control capabilities with RAG.

It’s worth noting that RAG and fine-tuning are NOT mutually exclusive. In fact, sometimes, they’re combined to leverage each of their strengths to create accurate, reliable LLM applications that you have control over.

For example, a medical assistance application can implement RAG to pull relevant medical and patient information from the database. At the same time, it can use fine-tuning to develop a curated set of common anticipated prompt-query pairs and give precise responses.


Beyond Vector Search: Why Contextual Understanding is Key for RAG

When it comes to implementing RAG, vector databases are considered one of the top contenders. The most crucial element of vector databases lies in vector search for retrieving reliable information from your database.

From query to vector similarity search

Vector search goes beyond keyword matching. It uses machine learning algorithms to capture the semantics of the query. The query is then converted into a numerical representation called vector embedding, which is matched with similar vector entries in the database through similarity algorithms. Through this process, vector search improves the relevancy of the data retrieved.

However, vector search can only match semantic similarity and generate responses based on an association of words. It lacks structure and can’t traverse the disparate pieces of vector data or connect the dots. So, it has trouble understanding deeper context and answering questions holistically. Enterprise-grade LLM applications often require LLMs to understand deeper context in order to produce accurate and reliable responses.


Combining Knowledge Graph & Vector Search to Unlock RAG’s Full Potential

Retrieval-augmented generation (RAG) helps keep AI’s feet on the ground, pulling in fresh, relevant data from an organization’s own treasure trove of knowledge. This smart combo means AI can chat away, providing spot-on answers, almost like it’s tapping into the company’s brain.

While vector search is great at matching semantic similarity, it lacks the context, structure, and reasoning required to give meaningful, holistic responses.

Microsoft Research found that using knowledge graphs along with vector search for RAG, an approach known as GraphRAG, greatly extends and enhances its capabilities.

Knowledge graphs are a structured data representation of a collection of entities (nodes) and relationships (edges) connecting them. It’s a massive, interconnected web of all the things your organization knows, laid out so AI can traverse from one fact to the next. Through inferential reasoning, knowledge graphs provide context-driven insights and a deeper understanding of the topics, overcoming the fundamental limitations of vector search. Neo4j knowledge graph with native vector search is the GraphRAG market leader.

A GraphRAG-powered LLM can match semantic similarity in text while simultaneously understanding the context from structured data, enabling it to return responses that truly address the question. Companies can build GraphRAG-powered LLMs that know the ins and outs of their operations and provide responses in a meaningful, holistic way.

Neo4j knowledge graph and native vector search for GraphRAG

If you’re assessing your tech stack for generative AI applications, the new report from Enterprise Strategy Group, Selecting a Database for Generative AI in the Enterprise, will be a useful resource. You’ll learn what to look for in a database for enterprise-ready GenAI applications. And you’ll discover why a vector-enabled knowledge graph is key to achieving enterprise-grade performance.