RAG for Startups: When You Actually Need It
RAG has become the default assumption for any AI feature involving custom data. But it's complex, expensive, and often unnecessary. Learn when you actually need it.
Every AI feature discussion eventually arrives at RAG. "We need the chatbot to answer questions from our documentation." "The assistant should reference our knowledge base." "Users should be able to query across all their data."
RAG-Retrieval-Augmented Generation-is the pattern that makes this work. You embed your documents into vectors, store them in a vector database, retrieve relevant chunks based on the user's query, and include those chunks as context for the LLM.
The problem: RAG has become the default assumption for any AI feature involving custom data. Teams implement RAG pipelines when simpler approaches would work better. They spend weeks on embeddings and vector search when their use case has 50 documents that could fit in a single prompt.
RAG is powerful. It's also complex, expensive, and often unnecessary.
What RAG Actually Solves
RAG exists to solve a specific problem: LLMs have knowledge cutoffs and don't know about your specific content. If you ask GPT-4 about your product's documentation, it will either confess ignorance or hallucinate plausible-sounding nonsense. RAG solves this by fetching relevant information from your data and including it in the prompt.
The Canonical RAG Pipeline
- Chunking: Split your documents into smaller pieces
- Embedding: Convert each chunk into a vector representation
- Indexing: Store the vectors in a vector database
- Query embedding: Convert the user's question into a vector
- Retrieval: Find chunks whose vectors are most similar to the query vector
- Generation: Include retrieved chunks in the LLM prompt to answer the question
When RAG Is Actually Necessary
Condition 1: Your Data Exceeds Context Limits
Modern LLMs have large context windows-128K tokens for GPT-4 Turbo, 200K for Claude. If your entire knowledge base fits in that window, you might not need RAG at all. Just include everything in the system prompt.
Condition 2: You Need Semantic Search Over Diverse Content
RAG shines when users need to find information across semantically diverse content where keyword matching fails. Vector similarity catches semantic relationships that keyword search misses.
Condition 3: Content Is Authoritative and Users Expect Grounded Answers
RAG is appropriate when your content is the source of truth, users expect answers based on that content specifically, and hallucinations would be problematic.
When Simpler Approaches Work Better
Most early-stage products don't meet the conditions above. Simpler approaches often suffice: putting content directly in the system prompt, keyword search with curated results, categorization-first routing, or structured data queries.
Key Takeaways
- Check the prerequisites first. Is your data too large for context windows? Do you need semantic search?
- Try simpler approaches. Static prompts and keyword search often suffice for early-stage products.
- If you need RAG, build incrementally. Start with a basic pipeline, measure quality, iterate.
- Invest in evaluation. You can't optimize what you don't measure.
The goal isn't to build RAG. The goal is to answer user questions accurately. Choose the simplest approach that achieves that goal.
