Retrieval-Augmented Generation (RAG) is the secret sauce that makes AI assistants grounded in your documents. Let's break down how it works.

What is RAG?

RAG combines the power of large language models (LLMs) with a retrieval system that searches your documents for relevant information. Instead of relying solely on knowledge from training data, the AI can "look up" information from your specific knowledge base.

The RAG Pipeline

Here's how Kodda implements RAG:

1. Document Ingestion

When you upload a document (PDF, DOCX, etc.), our system extracts the text content, preserving structure and formatting as much as possible.

2. Text Chunking

Long documents are split into smaller chunks (typically 500-1000 tokens each). This helps the retrieval system find the most relevant sections and ensures the LLM receives focused context.

3. Embedding Generation

Each chunk is converted into a vector embedding—a numerical representation of the text's meaning. Texts with similar meanings have similar vectors.

4. Vector Storage

These embeddings are stored in a vector database, organized by tenant for complete data isolation.

5. Semantic Search

When a user asks a question, it's converted to an embedding, and we search the vector database for the most similar chunks. This is called semantic search—finding results based on meaning, not just keyword matching.

6. Answer Generation

The retrieved chunks are fed to the LLM along with the user's question. The LLM generates a response grounded in this context, ensuring accurate, cited answers.

Why RAG Matters

Accuracy - Answers come from your actual documents, not hallucinations
Traceability - You can see which documents informed the answer
Freshness - Knowledge base can be updated without retraining
Privacy - Sensitive data stays in your controlled environment
Cost-effective - No need to fine-tune models on your data

Try It Yourself

Ready to build your own private AI? Sign up for Kodda, upload some documents, and experience the power of RAG firsthand. You can also learn how to set up an AI knowledge base so your assistant can answer directly from your documents.

Explore our RAG Use Cases or view Pricing Plans to get started.