Building RAG Pipelines Made Simple: A Practical Guide

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI applications that work with your documents. But setting up a RAG pipeline typically involves:

• Vector databases
• Embedding models
• Chunking strategies
• Retrieval algorithms
• Complex orchestration

What if it didn’t have to be this complicated?

What is RAG, Really?

At its core, RAG solves a simple problem: AI models have knowledge cutoffs and don’t know about your private documents.

The solution is elegant:

1. Store your documents in a searchable format
2. When a user asks a question, find relevant document chunks
3. Pass those chunks to the AI along with the question
4. The AI generates an answer grounded in your actual documents

Simple in concept. The implementation? That’s where teams typically spend weeks or months.

The Traditional RAG Setup

Here’s what a typical RAG implementation looks like:

┌─────────────────┐     ┌──────────────────┐
│   Documents     │────▶│  Text Extraction │
└─────────────────┘     └────────┬─────────┘
                                 │
                                 ▼
                        ┌──────────────────┐
                        │    Chunking      │
                        └────────┬─────────┘
                                 │
                                 ▼
                        ┌──────────────────┐
                        │   Embeddings     │
                        └────────┬─────────┘
                                 │
                                 ▼
                        ┌──────────────────┐
                        │  Vector Store    │
                        └────────┬─────────┘
                                 │
         User Query ────────────▶│
                                 │
                                 ▼
                        ┌──────────────────┐
                        │  Semantic Search │
                        └────────┬─────────┘
                                 │
                                 ▼
                        ┌──────────────────┐
                        │   LLM + Context  │
                        └────────┬─────────┘
                                 │
                                 ▼
                            Response

Each step requires decisions, infrastructure, and maintenance.

The Hard Parts

1. Text Extraction

PDF parsing alone can take weeks to get right. Different PDF generators produce different structures. Scanned documents need OCR. Tables are notoriously difficult.

2. Chunking Strategy

How do you split documents?

• Fixed token counts? (Loses context at boundaries)
• By paragraphs? (Varying chunk sizes)
• By semantic sections? (Complex to implement)
• Overlapping chunks? (Increases storage and query costs)

There’s no universal answer. It depends on your documents and use case.

3. Vector Database Operations

You need to:

• Choose a vector database (Pinecone? Weaviate? pgvector? OpenSearch?)
• Deploy and maintain it
• Handle scaling
• Manage indexes
• Deal with updates and deletions

4. Retrieval Quality

Semantic search isn’t perfect. You’ll need:

• Hybrid search (semantic + keyword)
• Reranking
• Metadata filtering
• Query expansion

A Simpler Approach

What if the infrastructure handled itself?

This is exactly what AWS Bedrock Knowledge Bases offer, and what we’ve integrated into Rockstead. Here’s how it works:

Automatic Pipeline

1. Upload a document → Text is automatically extracted
2. Knowledge Base creation → Chunking, embedding, and indexing happen automatically
3. Query → Semantic search returns relevant chunks
4. Response → AI generates grounded answers

No vector database to manage. No chunking algorithm to tune. No embedding pipeline to build.

How We Use It in Rockstead

When you create a workspace in Knowledge Base mode:

1. We provision an AWS Bedrock Knowledge Base automatically
2. Documents you upload are processed and indexed
3. When you chat, relevant chunks are retrieved automatically
4. You can switch between models while using the same Knowledge Base

The entire process takes minutes, not months.

When to Build Custom vs. Use Managed

Use Managed RAG (like Bedrock Knowledge Bases) When:

• You want to move fast
• Your documents are standard formats (PDF, Word, text)
• You don’t need extreme customization
• Infrastructure management isn’t your core competency

Build Custom RAG When:

• You have unique document formats
• You need specific chunking strategies for your domain
• You require hybrid search with custom weights
• You’re processing millions of documents with specific optimization needs

Best Practices for Either Approach

1. Evaluate Retrieval Quality First

Before worrying about the LLM, make sure your retrieval is working. Ask test questions and examine which chunks are being retrieved.

2. Compare With and Without RAG

Not every question needs RAG. Sometimes the model’s base knowledge is sufficient. Test both approaches.

3. Monitor Chunk Relevance

The most common RAG failure: retrieved chunks aren’t actually relevant. Build monitoring for this.

4. Test Multiple Models

Different LLMs handle retrieved context differently. Claude is excellent at synthesizing long contexts. Smaller models might struggle with too many chunks.

RAG Testing with Rockstead

This is why we built Rockstead with two modes:

Simple Mode

Documents are included directly in the prompt. Great for:

• Small documents
• Quick testing
• When you need the full document, not chunks

Knowledge Base Mode

Automatic RAG pipeline. Great for:

• Large document collections
• When only relevant sections matter
• Production-like testing

You can switch between modes and compare how different approaches work for your specific questions and documents.

Getting Started

Ready to build document-powered AI applications without the infrastructure headaches?

1. Join the Rockstead waitlist to get early access
2. Upload your documents when you get access
3. Compare models with your actual content
4. Iterate quickly without infrastructure blockers

Building RAG doesn’t have to be complicated. Let the infrastructure handle itself so you can focus on building great AI applications.