The Technology Behind AI That Knows Your Data

How can AI answer questions about your company’s policies, your codebase, or your customer data? It wasn’t trained on that information.

The answer is RAG: Retrieval-Augmented Generation.

The Problem RAG Solves

Large language models have impressive general knowledge. But they don’t know:

• Your company’s specific policies
• Your codebase and architecture
• Your customer data
• Recent information (after training cutoff)
• Proprietary documents and knowledge

Asking about these things gets you hallucinations or “I don’t have that information.”

How RAG Works

RAG combines retrieval (finding relevant information) with generation (AI creating responses):

1. Document Ingestion Your documents are processed and stored in a way AI can search.

2. Query Understanding When you ask a question, AI understands what you’re looking for.

3. Retrieval Relevant document chunks are found using semantic search.

4. Context Assembly Retrieved information is added to the AI’s context.

5. Generation AI generates a response based on the retrieved information.

6. Citation Sources are provided so you can verify.

The RAG Pipeline

[User Question]
       ↓
[Query Understanding]
       ↓
[Semantic Search] → [Vector Database]
       ↓
[Relevant Documents Retrieved]
       ↓
[Context + Question → LLM]
       ↓
[Answer with Citations]

Key Components

Document Processing: Documents are split into chunks (paragraphs or sections) that are meaningful on their own.

Embeddings: Each chunk is converted to a vector (list of numbers) that captures its meaning.

Vector Database: Embeddings are stored for fast similarity search.

Retrieval: When you ask a question, similar chunks are found by comparing embeddings.

Generation: The LLM uses retrieved chunks as context to answer your question.

Why RAG Works

Grounded in your data: Answers come from your documents, not training data.

Reduced hallucinations: AI answers based on evidence, not invention.

Current information: Updates when your documents update.

Verifiable: Citations let you check the source.

Building Effective RAG

Good chunking: Split documents at natural boundaries. Too small loses context. Too large wastes retrieval.

Quality embeddings: Use embedding models that understand your domain. General embeddings work, but domain-specific can be better.

Retrieval tuning: Find the right number of chunks to retrieve. Too few misses information. Too many overwhelms the LLM.

Prompt engineering: Tell the LLM how to use the retrieved context effectively.

RAG Pitfalls

Poor chunking: Information split across chunks doesn’t get retrieved together.

Retrieval failures: Question doesn’t match document language, so relevant content isn’t found.

Context overwhelm: Too much retrieved content confuses the LLM.

Missing citations: Users can’t verify answers without sources.

RAG in Calliope

Calliope makes RAG accessible:

Chat Studio: Connect documents, ask questions, get answers with citations.

AI Lab: Build custom RAG pipelines for your specific needs.

Langflow: Visual RAG pipeline construction.

Deep Agent: Agents that use RAG for research and analysis.

When to Use RAG

Good RAG use cases:

• Answering questions about your documents
• Customer support with product documentation
• Internal knowledge bases
• Code documentation queries
• Policy and procedure questions

Consider alternatives when:

• Documents change constantly (consider real-time integration)
• Answers require computation (consider tools)
• Questions span many documents (consider summarization first)

The RAG Checklist

For building RAG systems:

• Documents identified and accessible
• Chunking strategy defined
• Embedding model selected
• Vector database provisioned
• Retrieval parameters tuned
• Citation mechanism working
• Quality validated with test questions

RAG turns your documents into AI knowledge.

Build RAG systems with Calliope →