Back to blogLaunch App

RAG Frequently Asked Questions

Everything you need to know about Retrieval-Augmented Generation

What is RAG (Retrieval-Augmented Generation)?

RAG (Retrieval-Augmented Generation) is a technique that enhances large language models by retrieving relevant information from external knowledge sources before generating responses. Instead of relying solely on the model's training data, RAG systems first search a database or document collection for relevant context, then use that context to generate more accurate and up-to-date answers.

This approach combines the benefits of information retrieval with generative AI, resulting in responses that are grounded in factual, verifiable information rather than potentially outdated or incorrect training data.

How does RAG work?

The RAG pipeline consists of 7 main steps:

  1. Parsing: Extract and process content from documents (PDFs, HTML, etc.)
  2. Chunking: Split documents into smaller, meaningful segments for better retrieval
  3. Embedding: Convert text chunks into numerical vectors that capture semantic meaning
  4. Storage: Store embeddings in a vector database for efficient similarity search
  5. Retrieval: Search for relevant chunks based on user query similarity
  6. Reranking: Re-score and order retrieved results for maximum relevance
  7. Generation: Use retrieved context with an LLM to generate the final answer

When should I use RAG?

RAG is ideal for:

  • Answering questions about private or proprietary data
  • Providing up-to-date information beyond the model's training cutoff date
  • Reducing hallucinations by grounding responses in verified sources
  • Building chatbots with domain-specific knowledge
  • Creating question-answering systems over large document collections
  • Implementing semantic search with natural language queries

RAG vs Fine-tuning: Which should I choose?

CriterionRAGFine-tuning
CostLow (no model training)High (requires GPU training)
Data updatesReal-time (just update DB)Requires retraining
TransparencyHigh (can cite sources)Low (black box)
Use caseKnowledge retrievalStyle, tone, format learning
Hallucination riskLower (grounded in data)Higher (memorized patterns)

Best practice: Use RAG for knowledge augmentation and fine-tuning for behavior modification. Many production systems combine both approaches.

What vector database should I use for RAG?

Popular vector database options include:

  • ChromaDB: Lightweight, great for prototyping and local development
  • Pinecone: Managed service, scales well for production
  • Weaviate: Open-source with hybrid search capabilities
  • Qdrant: High-performance with filtering support
  • Milvus: Enterprise-grade, highly scalable

Choose based on your scale, budget, and whether you prefer managed or self-hosted solutions.

How can I improve RAG accuracy?

Key strategies to improve RAG performance:

  • Better chunking: Use semantic chunking instead of fixed-size splits
  • Hybrid search: Combine semantic search with keyword matching (BM25)
  • Reranking: Add a reranking step to improve result quality
  • Query expansion: Reformulate queries for better retrieval
  • Metadata filtering: Use document metadata to narrow search scope
  • Better embeddings: Choose domain-specific embedding models
  • Retrieval evaluation: Measure and optimize retrieval metrics (MRR, NDCG)

What are common RAG implementation challenges?

  • Context window limits: Retrieved chunks must fit within the model's context length
  • Chunk size optimization: Finding the right balance between granularity and context
  • Retrieval relevance: Ensuring retrieved documents are actually relevant to the query
  • Multi-hop reasoning: Handling queries that require information from multiple sources
  • Cost management: Balancing embedding costs, storage, and inference costs
  • Latency: Keeping response times acceptable for production use

How much does RAG cost to run?

RAG costs typically include:

  • Embedding generation: One-time cost per document, usually $0.0001-0.001 per 1K tokens
  • Vector storage: $0.096-0.40 per million vectors per month (varies by provider)
  • LLM inference: $0.03-0.60 per 1M tokens depending on model size
  • Infrastructure: Compute for retrieval and reranking

For most applications, RAG is significantly cheaper than fine-tuning, especially when data changes frequently.

Ready to build your RAG system?

Explore our step-by-step guides covering every aspect of the RAG pipeline

View GuidesTry Ailog