From Hallucination to Accuracy: How RAG (Retrieval Augmented Generation) Is Reshaping the 2024 Generative AI Landscape

In recent years, advances in large language models have driven remarkable progress in natural language processing, rapidly accelerating the practical use of generative AI. However, these powerful models still have challenges to overcome — one of the biggest being "hallucination," where large language models (LLMs) generate inaccurate, false, or unsubstantiated information. This article focuses on Retrieval Augmented Generation, a highly useful, widely adopted technique for tackling this problem.

Regular LLMs are static and can't incorporate updated or the latest data — their training data is "frozen" at a point in time and can't respond to subsequent events. LLMs are also trained for general tasks and lack domain-specific knowledge, so they can't accommodate a company's own data or requirements.

1. What Is RAG (Retrieval Augmented Generation)

1.1. What Is Retrieval Augmented Generation (RAG)

Researchers at Meta AI (then Facebook) introduced a technique called "Retrieval Augmented Generation (RAG)" to tackle knowledge-intensive tasks — combining an information retrieval component with a text-generator model. It's fine-tunable, letting internal knowledge be modified efficiently without retraining the entire model.

RAG's basic flow is to extract relevant passages through document search and pass them to an LLM as a prompt. Here, among RAG's common techniques, we converted sentences into vectors, stored them in a vector database (Vector DB), then performed a similarity search.

1.2. 4 Tips for Improving the Accuracy and Performance of Generative AI Applications

Products built on LLMs — like OpenAI's ChatGPT or Anthropic's Claude — are impressive, but come with certain shortcomings. Today's LLMs are powerful, yet carry several drawbacks, as shown below.

1. LLMs are static. In other words, they're "frozen in time" and can't access the latest information. Updating their massive training datasets isn't practical.
2. LLMs lack domain-specific knowledge. They're trained for general tasks and aren't familiar with a company's private data.
3. These models function as "black boxes." It's not easy to understand which sources an LLM considered in reaching its conclusion.
4. These models are also inefficient and expensive to build. Constructing and deploying a foundation model requires substantial financial and human resources, which not every organization can afford.

Unfortunately, these issues hurt the accuracy of generative AI applications. In business applications with stricter requirements than a typical chatbot demo, using an LLM "as is" without modification beyond prompting degrades performance, especially for context-dependent tasks.

In this post, we'll examine why Retrieval Augmented Generation (RAG) is recommended to address these shortcomings, explain how RAG's mechanism works in detail, and explore why many companies choose RAG to improve the performance of their generative AI applications.

2. Three Challenges Facing Today's Large Language Models (LLMs)

ChatGPT's training data "cutoff point" was September 2021. Ask it about something from last month, and it won't just fail to answer factually — it may come up with a very convincing outright lie. We usually call this behavior "hallucination."

LLMs like ChatGPT feel so brilliant because they've seen an enormous volume of human creative output — open-source code equivalent to entire companies, whole libraries of books, lifetimes of conversation, scientific datasets. But crucially, this core training data is static.

After OpenAI finished training the foundation model (FM) behind ChatGPT in 2021, it received no further world updates. It remains unaware of major world events, weather systems, new laws, sports results, and, as the image above shows, even the latest car operating instructions.

2.1. The Static Nature of Large Language Models (LLMs)

RAG is an architecture that retrieves the latest, context-specific information from an external database and makes it available to the LLM during generation. By storing your own business data and world information for the LLM to use, RAG reduces hallucination risk and improves generative AI application performance and accuracy.

2.2. Lack of Knowledge in Specific Domains

The second major shortcoming of today's LLMs is that, despite an excellent knowledge corpus, they don't grasp your business's details, requirements, customer base, or the context the application runs in (e.g., email). RAG addresses this by giving the LLM additional context and factual information at generation time — anything from customer records to script lines, product specs, current inventory, or audio. The LLM uses this content to generate an informed answer.

2.3. Retrieval Augmented Generation Improves Transparency

RAG was developed to address the challenges of the latest information and domain-specific data. It also lets generative AI applications cite their sources — much like citing important data sources in a research paper.

Using RAG makes it easier to audit and understand a generative AI application's internal workings. End users can directly access the source documents the application used and verify their reliability. This integrated approach ultimately enables reliable information and effective decision-making in the legal industry and beyond.

3. Three Key Reasons to Adopt RAG

Besides RAG, there are three other main options for improving generative AI application performance. Let's examine them to understand why so many companies still adopt RAG as their primary approach today.

3.1. Building Your Own Foundation Model

OpenAI's Sam Altman has estimated it costs about $100 million to train the foundation model behind ChatGPT.

Not every model needs that level of investment, but ChatGPT's price tag highlights cost as a major hurdle to building a sophisticated model with today's technology.

Beyond raw computing costs, you'll also face a talent shortage. Tackling the technical challenges of building such a model requires a specialized team of ML PhDs, top-tier systems engineers, and skilled operations staff — and other AI companies worldwide are chasing the same scarce talent.

Another challenge is acquiring, sanitizing, and labeling the datasets needed for a capable foundation model. For example, a legal e-discovery firm training a model to answer questions about legal documents would need legal experts to spend many hours labeling training data.

Even with sufficient capital, the right team, the right labeled datasets, and the technical hurdles of production hosting overcome, success still isn't guaranteed. The industry has already seen ambitious AI startups rise and fall, and more failures likely lie ahead.

3.2. Fine-Tuning a Model on Domain Data

Fine-tuning is the process of retraining a foundation model on new data. It can be cheaper than building a foundation model from scratch, but shares many of the same drawbacks — rare, deep expertise and sufficient data are still required, and it doesn't eliminate the cost and complexity of hosting a model in production.

Now that LLMs can be paired with vector databases for context retrieval, fine-tuning is no longer practical. Some LLM providers, including OpenAI, no longer support fine-tuning their latest-generation models.

Fine-tuning is an outdated way to improve LLM output. It required costly, repetitive labeling work by subject-matter experts, plus continuous monitoring for quality drift and accuracy deviations caused by a lack of regular updates or shifting data distribution.

As data changes over time, a fine-tuned model's accuracy can decline too, requiring more costly data labeling, ongoing quality monitoring, and repeated fine-tuning.

Imagine having to update the model every time you sell a car just to keep your generative AI application's inventory data current.

3.3. Prompt Engineering Alone Isn't Enough to Reduce Hallucination

Prompt engineering means testing and fine-tuning the instructions given to a model so it behaves as intended.

It's also the cheapest option for improving accuracy, since you can quickly update the LLM's instructions with just a few code changes.

While it refines an LLM's responses, it can't provide new or dynamic context, so the application still lacks up-to-date context and remains prone to hallucination.

4. How Does Retrieval Augmented Generation (RAG) Work?

4.1. How Retrieval Augmented Generation Works

We explained how, at generation time, RAG passes additional relevant content from a domain-specific database to the LLM through a "context window," alongside the original prompt.

An LLM's context window is its field of view at a given moment. RAG is like holding up cue cards with key points, helping it generate more accurate responses that incorporate important data.

To understand RAG, you first need semantic search. Rather than simply matching keywords, it tries to find the true meaning behind a query and retrieve relevant information — delivering results that fit the user's intent, not just their exact words.

This diagram shows how a vector database is built from domain-specific proprietary data — running it through an embedding model to convert it into vectors.

An embedding model is a type of LLM that converts data into vectors — arrays of numbers. Above, we're converting a user manual for operating the latest Volvo, but the data could just as well be text, images, video, or audio.

The key thing to understand: a vector represents the meaning of the input text, much like another person grasps the essence of what's said when spoken aloud. Data is converted into vectors so a computer can retrieve semantically similar items based on numerical representations of stored data.

Next, the vectors are stored in a vector database. Recall that creating vectors, ingesting them, and updating the index in real time can solve an LLM's currency problem in a GenAI application.

For example, you could write code to automatically create vectors for the latest products and update the index every time a new product launches. A support chatbot can use RAG to retrieve the latest product availability and data about the customer it's chatting with.

4.2. Using a Vector Database to Query Data in Natural Language

A vector database embeds numerical representations of target data, which is what enables semantic search. Vector databases really shine in semantic search use cases, where end users write vague queries in natural language.

4.3. Semantic Search: How Queries Are Converted to Embeddings and Matched Against a Vector Database

First, your own data is converted into embeddings. When a user makes a query or asks a question, that natural-language search term is also translated into an embedding.

These embeddings are then sent to the vector database. The database performs a "nearest neighbor" search to find the vectors that best match the user's intent. Once the vector database returns relevant results, the application feeds them to the language model via the context window to guide the generation task.

The language model can now access the most relevant, well-grounded facts from the vector database. This lets the application deliver accurate answers to users. RAG reduces the likelihood of hallucination.

5. Frequently Asked Questions (FAQ)

6. Conclusion

Semantic search and Retrieval Augmented Generation deliver an outstanding experience to end users by improving the relevance of generative AI responses. Unlike building a foundation model, fine-tuning an existing model, or relying solely on prompt engineering, RAG efficiently and safely tackles both currency and context-specific problems at the same time. Its core purpose is to provide detailed, situation-aware answers to questions that require access to personal data.

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