1. What is RAG and Why It Matters
Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval with language model generation. It allows Large Language Models (LLMs) to generate responses that are grounded in external, up-to-date, and domain-specific information, rather than relying solely on what they learned during training.
For example, imagine you have thousands of PDF files containing product manuals or research papers. A RAG system can take a user’s question, search through these PDFs, find relevant content, and generate an accurate, context-based answer. This eliminates the need to fine-tune the model on your data and allows the system to scale easily as new information becomes available.
2. The Core Architecture of a RAG System
A RAG system generally consists of two main modules, each made up of multiple interconnected components.
Module 1 — Knowledge Preparation
This module is responsible for gathering, processing, and storing your data in a form that can be efficiently searched and retrieved later.
It includes four main steps:
- Data Ingestion
- Data Chunking
- Embedding Generation
- Vector Storage
Module 2 — Query and Generation
This module handles the user interaction, retrieval, and the final generation of responses.
It includes:
5. Retrieval Process
6. Prompt Construction
7. LLM Response Generation
3. Step-by-Step Explanation of Each Component
3.1 Data Ingestion
Data ingestion is the first and most critical step in building any RAG pipeline.
It refers to collecting and loading data from various sources into your system so that it can later be processed and converted into meaningful representations.
The data source can be highly diverse — for example:
- PDF files containing research papers, reports, or manuals.
- Excel or CSV files holding tabular data such as customer records or sales logs.
- Text or JSON files that store unstructured or semi-structured textual information.
- Web pages or URLs, from which you can extract textual content using web scrapers.
- Audio or video transcripts, which can be converted to text using speech-to-text technology.
- Images, which can be processed using Optical Character Recognition (OCR) to extract text.
The key idea is to bring all forms of content into a consistent textual format that can later be divided into smaller, more manageable parts.
3.2 Data Chunking (Text Splitting)
Once the data has been loaded, the next task is to split it into smaller pieces called chunks.
This process is called data chunking or text splitting.
The reason for chunking is that LLMs have a context length limitation, meaning they can only process a fixed number of tokens (words or characters) at once. If you try to feed the model an entire document that exceeds this limit, it won’t be able to process it efficiently or correctly.
Chunking solves this problem by:
- Dividing long texts into smaller, logical sections such as paragraphs or sentences.
- Ensuring there is a small overlap between chunks so that context is preserved across boundaries.
- Attaching metadata (like file name, page number, or section title) to each chunk so you can trace back where the content came from later.
By splitting large documents into smaller chunks, you make it easier to perform vectorization and retrieval in later stages while preserving the semantic flow of information.
3.3 Embedding Generation
After chunking, each text segment must be converted into a numerical vector representation, known as an embedding.
An embedding captures the semantic meaning of the text — that is, it represents similar sentences or ideas with similar vectors in a high-dimensional space. This enables algorithms to find “similar” chunks when a user query is received.
For example, the sentences “What is LangChain?” and “Explain the LangChain framework.” will have embeddings that are close to each other in vector space.
Different models can generate embeddings:
- OpenAI Embeddings — a hosted service that produces high-quality vector representations.
- Hugging Face Models — open-source alternatives like
all-MiniLM-L6-v2that work locally. - Google Gemini Pro Embeddings — another cloud-based solution.
Each embedding model may differ in terms of dimensionality, cost, speed, and accuracy. The goal is to choose one that fits your project’s performance and resource requirements.
3.4 Vector Store Database
Once embeddings are created, they need to be stored in a specialized database called a Vector Store or Vector Database.
A vector database stores both:
- The embedding vectors (numerical arrays), and
- The associated metadata (original text, document ID, page number, etc.).
It also provides fast similarity search capabilities, allowing you to find which stored vectors are most similar to a new query vector.
Common vector databases include:
- FAISS (Facebook AI Similarity Search) — a fast and local option for small projects.
- ChromaDB — an open-source database designed for LangChain integrations.
- Astra DB, Pinecone, Weaviate, and Milvus — cloud-based vector databases suitable for large-scale or distributed systems.
This database is the core memory of your RAG system, allowing you to quickly retrieve the most relevant pieces of information when a user asks a question.
3.5 Retrieval Process (Retrieval Chain)
Once your data is stored as vectors, the next step is to query this database whenever a user asks a question.
This process is called retrieval and is implemented using a retrieval chain.
Here’s what happens:
- The user provides a question, such as “What are the steps to create embeddings in LangChain?”.
- The system converts this question into its own embedding vector.
- It compares the question vector with all document vectors stored in the vector database.
- Using similarity search algorithms such as cosine similarity, it finds the most semantically relevant chunks.
- The top results (for example, the top 3 or top 5 chunks) are returned as context.
This retrieved context represents the most relevant pieces of information related to the user’s query and forms the foundation for the final answer generation.
3.6 Prompt Construction (Prompt Template)
Before passing information to the LLM, we need to construct a prompt — a structured text that instructs the model on how to behave and how to use the retrieved information.
A prompt template typically includes:
- A system instruction (for example, “You are an AI researcher. Provide accurate, evidence-based answers.”).
- The retrieved context (the chunks obtained from the vector database).
- The user’s question itself.
These three components are combined into one well-formatted text input that is fed to the LLM.
Prompt engineering plays a vital role in determining the quality of the model’s output.
A well-designed prompt ensures that:
- The model stays grounded in the provided context.
- The model avoids hallucination (making up facts).
- The answer is concise, structured, and relevant.
3.7 LLM Response Generation
Finally, the constructed prompt is passed to a Large Language Model (LLM) such as:
- OpenAI GPT models,
- Google Gemini,
- Anthropic Claude, or
- Open-source models like Llama 3 or Falcon.
The model uses the context provided to generate a meaningful, fact-based answer.
The output is then presented to the user, optionally along with source references or citations from the retrieved documents.
This final step transforms the retrieved data into a human-like natural language answer — completing the RAG cycle.
4. Textual Diagram — Complete RAG Workflow
Below is a text-based diagram showing how all components connect from start to finish.
┌─────────────────────────────┐
│ Data Source │
│ (PDFs, CSV, Text, Web) │
└──────────────┬──────────────┘
│
▼
┌──────────────────────────────┐
│ 1. Data Ingestion │
│ (Load & preprocess files) │
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ 2. Data Chunking │
│ (Split into smaller parts) │
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ 3. Embedding Creation │
│ (Convert text → vectors) │
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ 4. Vector Store Database │
│ (Store & index embeddings) │
└──────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ QUERY PHASE │
└─────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ 5. Retrieval Chain │
│ (Find similar chunks) │
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ 6. Prompt Construction │
│ (Combine context + question) │
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ 7. LLM Generation Step │
│ (Answer based on context) │
└──────────────────────────────┘
│
▼
┌─────────────────────────────┐
│ Final Output │
│ (Accurate, grounded answer) │
└─────────────────────────────┘
5. Key Takeaways
- RAG = Retrieval + Generation — it bridges the gap between your private data and the reasoning power of LLMs.
- Data Ingestion ensures all sources are normalized and available for processing.
- Chunking enables efficient and context-aware retrieval within model limits.
- Embeddings represent text meaning numerically, enabling similarity-based search.
- Vector Store serves as the memory backbone of your system.
- Retrieval Chains fetch relevant context dynamically at query time.
- Prompt Templates define the model’s behavior and help produce factual results.
- LLM Generation creates a natural-language answer that feels conversational but is grounded in real data.