Introduction to RAG: Retrieval-Augmented Generation

Large language models are powerful, but they have a fundamental limitation: they only know what was in their training data. RAG (Retrieval-Augmented Generation) solves this by giving AI access to your specific knowledge at query time.

The Problem RAG Solves

When you ask a question to a standard LLM:

• It can only use knowledge from its training data
• It may hallucinate if it doesn't know the answer
• It has no access to your proprietary information
• It can't cite sources for its claims

RAG addresses all of these by retrieving relevant documents first, then generating responses grounded in that retrieved context.

How RAG Works

The Basic Architecture

User Query → Retrieval → Augmentation → Generation → Response

Step 1: Indexing (preparation) Your documents are processed and stored in a searchable format:

1. Documents are chunked into manageable pieces
2. Each chunk is converted to a vector embedding (a numerical representation)
3. Embeddings are stored in a vector database

Step 2: Retrieval (at query time) When a user asks a question:

1. The query is converted to a vector embedding
2. Similar document chunks are retrieved from the database
3. The most relevant chunks are selected

Step 3: Augmentation Retrieved context is combined with the user's question:

"Given the following context: [retrieved documents]
Answer this question: [user query]"

Step 4: Generation The LLM generates a response based on both the query and the retrieved context, grounded in your actual data.

Key Components

Vector Embeddings

Embeddings transform text into high-dimensional numerical vectors where semantically similar content clusters together. This enables semantic search—finding documents by meaning, not just keyword matching.

Vector Databases

Specialized databases optimized for storing and searching vector embeddings:

• Pinecone: Fully managed, easy to use
• Weaviate: Open source, feature-rich
• Chroma: Lightweight, developer-friendly
• pgvector: PostgreSQL extension (familiar tooling)

Chunking Strategies

How you split documents affects retrieval quality:

• Fixed-size chunks: Simple but may split context
• Semantic chunking: Split at natural boundaries
• Hierarchical chunking: Multiple granularity levels
• Overlapping chunks: Preserve context at boundaries

Retrieval Methods

Beyond basic vector similarity:

• Hybrid search: Combine semantic and keyword matching
• Reranking: Use a secondary model to refine results
• Query expansion: Reformulate queries for better retrieval
• Metadata filtering: Narrow search by document attributes

When to Use RAG

RAG is ideal when:

• You need AI responses grounded in specific documents
• Your knowledge base changes frequently
• Users need citations and source transparency
• You want to avoid fine-tuning costs and complexity
• Accuracy matters more than speed

RAG may not be the best choice when:

• Responses must be extremely fast (retrieval adds latency)
• Your use case is simple enough for a pre-trained model
• You need the model to learn new behaviors (not just new facts)

RAG vs. Fine-Tuning

Many production systems combine both: fine-tuned models with RAG for knowledge.

Common Challenges

Retrieval Quality

The biggest factor in RAG performance is retrieval quality. If the wrong documents are retrieved, the response will be wrong.

Solutions:

• Invest in chunking strategy
• Use hybrid search
• Implement reranking
• Test retrieval separately from generation

Context Window Limits

LLMs have limited context windows. You can't just retrieve everything.

Solutions:

• Careful chunk selection
• Summarization of retrieved content
• Hierarchical retrieval approaches

Hallucination Despite Context

Models can still hallucinate even with relevant context.

Solutions:

• Instruction tuning to follow context
• Verification steps
• Citation requirements in prompts

Building Your First RAG System

Minimal Viable RAG

1. Prepare documents: Clean and chunk your content
2. Create embeddings: Use an embedding model (e.g., OpenAI's text-embedding-ada-002)
3. Store in vector DB: Index your embeddings
4. Build retrieval: Implement query → embedding → search → results
5. Augment prompts: Combine retrieved context with user queries
6. Generate responses: Call your LLM with the augmented prompt

Evaluation Metrics

Measure RAG performance on:

• Retrieval precision: Are retrieved documents relevant?
• Retrieval recall: Are all relevant documents found?
• Answer accuracy: Are generated answers correct?
• Answer groundedness: Are answers supported by retrieved content?
• Latency: Is the system fast enough for your use case?

Advanced Topics

As you mature your RAG implementation, explore:

• Multi-vector retrieval: Multiple embeddings per document
• Agentic RAG: Agents that decide when and how to retrieve
• Graph RAG: Knowledge graphs combined with vector search
• Multimodal RAG: Retrieval across text, images, and other modalities

Next Steps

RAG is foundational to most enterprise AI applications. Start simple, measure carefully, and iterate based on real-world performance.

Need help implementing RAG for your organization? Get in touch to discuss your knowledge management challenges.