Introduction to RAG (Retrieval-Augmented Generation)

I’ve been diving into Generative AI lately, and one thing is clear: if you’ve spent any time with LLMs, you’ve probably run into their limitations. You ask an LLM a highly specific question about a new software library, your company's internal documents, or breaking news, and it either politely declines to answer or confidently makes up a complete lie.

The LLMs don't know about the recent information because they are limited to knowledge available till the time they were trained. And they don’t know about your company's internal info or documents because they were never trained on them and it is not safe because anyone with access to that model can get your internal information.

So, if you want the LLM to answer the questions based on information or data you provide to it without making it available publicly, how will you do it? Well there are plenty of ways by which you can make this possible. The ways are:

1. Fine-tuning: With the fine-tuning you can train the existing AI model on your private data and users can then ask questions based on that data. It is a high computation task, time consuming and is very costly.

2. Large Context Window Prompting: You provide all the information in your prompt and tell the LLM to answer based on your provided data only.

Although these methods can work they have their own limitations. Fine-tuning the LLM on your data is very costly and it requires high resources for computation and if your information is updated you have to fine-tune again which makes it an even more expensive approach. The LLMs have limited context window and when you query a LLM, you are not only sending a query to the user, you also send other stuff like system prompt and chat history along with your query. So, if you give large no. of information or data in prompt, you might hit the context window limit

Now, we need a solution which is cost and computation efficient and you can easily update your information without hitting the context window limit. This is where RAG comes into the picture.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI framework that improves LLM accuracy by fetching relevant, up-to-date information from external, trusted sources (like databases or company documents) before generating a response. It prevents misinformation ("hallucinations") by grounding the answer in verified data rather than relying solely on training data.

The RAG is implemented in two phases:

1. Indexing Phase: Indexing phase involves gathering the information or documents, generating embeddings and storing it in vector stores or vector DB.

2. Retrieval Phase: This phase involves retrieving the relevant information from vector store or DB and providing it to LLM for generating response based on the user's query.

Imagine you are hosting a major award show and you have a massive script that contains every single detail. This script includes the names of all the winners, the order of every performance, and every word you need to say. The problem is that the script is far too long for you to memorize entirely, and carrying the whole book on stage is too clumsy when you need to find a specific name quickly.

To solve this, you go through the script and pick out the most important facts to write on small cue cards. Each card contains just one specific piece of information, like the winner of a single category. You keep these cards organized in a small box so you can grab the right one at the right moment. When it is time to announce the Best Actor award, you don't flip through the whole script. You simply pull out the specific cue card for that award, read the name, and announce it to the crowd.

This process is exactly how RAG works. The long script represents your massive collection of data or documents. The difficulty of memorizing that script is the same as the limit on how much information an AI can "remember" or process at once. Breaking the script down into small cue cards is the same as turning your documents into small chunks. Storing those cards in a box is like putting those chunks into a vector store. Finally, finding the right card and reading the winner's name is the same as the AI finding the most relevant piece of information and using it to give you a perfect answer.

Components of RAG

A RAG pipeline consists of various components which makes the implementation of indexing phase and retrieval phase possible.

Document Ingestion & Indexing: This is responsible for gathering the information or documents from various sources and loading them.

Text Splitters: This is a crucial part of a RAG pipeline because this is responsible for dividing your large documents into smaller and manageable chunks. If we don’t convert large documents into small chunks it will not give us good results at the time of retrieval.

Vector Embeddings: This phase involves converting these small chunks into vector embeddings with the help of embedding models. This will help us to capture the semantic meaning of our texts.

Vector Stores & VectorDB: Stores embeddings and enables similarity search for fast information retrieval.

Retriever: Finds and returns the most relevant chunks from the database based on query similarity.

Prompt Augmentation Layer: Combines retrieved chunks with the user’s query to provide context to the LLM.

The Output: The LLM reads the context and provides highly accurate, hallucination-free answers.

Disadvantages of RAG

From the above discussions the advantages of RAG is clear but everything has its own limitations and disadvantages, and so does RAG. Here are some key disadvantages of RAG:

• Retrieval Dependency and Quality: RAG depends entirely on the retriever component. If the retriever fetches irrelevant, outdated, or incomplete data, the LLM will generate poor-quality, inaccurate, or "hallucinated" answers.

• Performance and Latency Bottlenecks: The extra step of searching a database (vector retrieval + generation) increases latency, making it unsuitable for some real-time applications.

• System Complexity and Maintenance: Implementing and maintaining a RAG system involves managing databases, embedding models, and retrieval techniques. Updating knowledge bases requires complex re-indexing and re-embedding.

• Data Security and Privacy Risks: RAG systems may expose sensitive or proprietary internal data to unauthorized users if access controls are not robustly implemented.

• Contextual Understanding Failures: RAG systems can struggle with complex, interdisciplinary queries or connecting disparate pieces of retrieved information, leading to incoherent outputs.

• Cost: Running RAG systems can be expensive, requiring both vector storage infrastructure and increased compute for the retrieval and generation processes

• Chunking Errors: Improperly splitting documents can cause vital information to be missing or disjointed.

• Debugging Difficulties: Due to the multiple moving parts (retriever + LLM), identifying the root cause of a poor answer is complex.

Conclusion

In short, RAG is a practical way to make AI smarter and more useful for your specific needs. Instead of spending a lot of time and money trying to "teach" an AI everything from scratch, RAG simply gives the AI the ability to look up facts from your own private documents before it answers a question, much like taking an open-book test.