Unlocking the Power of RAG: Enhancing AI with Retrieval-Augmented Generation

Terms Covered: RAG , Ingestion/ Indexing, Retrieval , Context Window , Chunking, Embeddings, Vector Embeddings, LLM Model , Vector databases, Meta-data , Prompt , LangChain, RAGTools

What is RAG ❓

Retrieval-Augmented Generation

Its basically, Retrieving (Fetching) Relevant information from some External data source and providing it as a context for LLMs to Augement ( Increase ) the model’s base Knowledge to Generate Improved Output response by LLMs .

RAG is Most Useful when you want to Response from some Information ( which it doesn’t have Access to , that can be either realtime data, your personalized Data , Data came after Model’s Knowledge Cutoff time, etc. )

Use-Cases Examples:

• You want to know about React 19, and your LLM model is trained till React 18

• You want to Analyze and use Data from your organization , which is stored in some kind of database.

• You want to question from a big PDF File, you don’t have time to go through it completely, but you need information from it.

Why RAG ❓

For LLM models , we have two approaches to make them usable for any Business/ Application Layer:

1. Fine Tuning - Take some base LLM Model and train it on our data

2. RAG - Providing Relevant Data as Context/ Prompt to LLM Models for our use Case on the spot

That’s, why RAG is application focused and usable for most Companies .

How RAG Works ?

There are steps in its usage process: but mainly three parts: INGESTION | RETRIEVAL | GENERATION

Inputs : External Data Sources - Pdf, Image, Text, Database, Videos, Books, Web etc. + User Prompt

INGESTION / INDEXING:

First we load our data, then

1. Splitting : CHUNKING

   • First we do Chunk data down , this process is called Chunking, why we do that ?

   • To break down Bigger data, as all LLMs have their own Context Window - How much context they can have at a particular moment - similiar to our Memory Capacity, like how much we can remember at a point about anything ( like a topic - it depends on individuals) - similarily, LLMs have their own Context Window Limits, so it cannot used our Terabytes of data as context , and this context changes with user Prompt too, so we chunk down data to make it easier for Models to Get only specific data according to user Prompt .

   • This Chunking is the most important step - as here u have to decide carefully and smartly how to chunk down your data, as if u provide bigger chunks that might lead to Hallucinations to Model, if u provide smaller chunks that may result in Data loss.

2. Embedding :

   • here we create embeddings of those chunks , to store them with some metadata, so that their retrieval becomes feasible at time of data fetching.

   • now these embeddings can be as simple as Texts, and can be as complex as Vector Embeddings leading to Graphs which again lead to some Vector Embeddings

   • Vector Embeddings are most common, as below that normal text, means your data is not that big , that this is not Optimal technique to use, Vector Embeddings - Giving Semantic Meaning in a 3d Space

3. Storing : Vector Database

   • here we store those embedding’s for using later on , for that we have multiple databases available like Pinecone, Qdrant , Chroma etc.

   • 

Till here , we are done with INGESTION/ INDEXING part, basically, ingesting the external data, and we are ready for user Prompt now

RETRIEVING :

1. On Receiving user prompt, We again create Vector embeddings of it, and sent in Vector Database (where we have Pre-Organized useful Data) to search for Relevant data there, we take out Relevant data from database

2. then it depends, if in metaData of those Vector Embeddings , if we had, directly data available then we are ready with Relevant Context to pass to our MODEL , but if not then we need to search through that metaData in our Original Data Source to fetch Actual Relevant Data to be used as Context now

GENERATION:

1. After, fetching useful Context, we Provide it to our LLM along with User Query, and now LLM have its own PreTrained Data Knowledge + Useful Knowledge Provided from External Data and User Query - It just has to create his Response, that’s how whole RAG works.

🚀 Useful Tools for RAG:

1**. LangChain**

• What it does: Framework that connects LLMs with external data (documents, databases, APIs) easily.

• Why it's useful: Simplifies building RAG pipelines — from retrieval to generation — using modular components like retrievers, chains, and agents.

2. LlamaIndex (formerly GPT Index)

• What it does: Helps structure your private or custom data into indices that LLMs can query easily.

• Why it's useful: Makes it super easy to connect LLMs to your personal or enterprise datasets for custom retrieval in RAG workflows.

3. FAISS (Facebook AI Similarity Search)

• What it does: A library for fast, efficient similarity search across massive document embeddings.

• Why it's useful: Critical for quickly finding the most relevant documents when you're doing retrieval at scale.

4. Pinecone

• What it does: Fully managed vector database service built for production-grade similarity search.

• Why it's useful: Saves time on infrastructure; scalable, real-time document retrieval with very low latency.

5. Chroma

• What it does: Open-source embedding database designed for AI applications.

• Why it's useful: Easy to use locally during prototyping or small-scale RAG projects without needing cloud services.

6. Haystack (by deepset)

• What it does: Framework for building end-to-end search and question-answering systems over your data.

• Why it's useful: Focused on retrieval-augmented pipelines, offers fine-tuning, and works well for complex RAG tasks like document QA or multi-hop retrieval.

7. OpenAI Embeddings API

• What it does: Generates vector representations of text for semantic similarity searches.

• Why it's useful: Essential for converting your documents into embeddings that you can later retrieve relevant pieces from in RAG.

8. Qdrant

• What it does: Open-source, high-performance vector database designed for storing and searching embeddings.

• Why it's useful: Provides efficient and scalable semantic search with filtering support, making it ideal for complex RAG systems where you might want fine-grained retrieval (for example, filter by metadata like date, category, tags while searching).

Summary:

What RAG Solves :

How Rag Works ?

Conclusion:

Just Explained what i understood about RAG! if u find it useful then don’t forget to like this Article &

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Credits:

Credits: I am very grateful to ChaiCode for Providing all this knowledge, Insights , Deep Learning about AI : Piyush Garg Hitesh Choudhary

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