"Should I use file search or RAG?"
This question comes up constantly as teams evaluate AI tools and build knowledge-based applications. The confusion is understandable—both involve finding information in documents. But they solve fundamentally different problems, and choosing wrong can mean building an application that frustrates users instead of helping them.
Let's clear up the confusion.
The Core Difference
File search helps you find documents. You search, you get a list of files, you read them yourself.
RAG (Retrieval Augmented Generation) helps you get answers. You ask a question, the system finds relevant information, and an AI generates a response using that information.
Think of it this way:
- File search is like a librarian pointing you to the right bookshelf
- RAG is like a research assistant who reads the books and writes you a summary
Both are valuable. They're just different tools for different jobs.
How File Search Works
Traditional file search operates on keywords and metadata:
- You enter search terms
- The system matches those terms against document content and metadata
- You get a ranked list of documents
- You open and read the relevant documents yourself
Modern file search has gotten sophisticated. Google Drive, Dropbox, SharePoint—they all offer search that understands synonyms, handles typos, and ranks by relevance. Some even use AI to improve results.
But the output is always the same: a list of documents you need to read.
When File Search Works Well
- Navigating known documents: "Where's that Q3 report?"
- Browsing and discovery: "What do we have about the Chicago project?"
- Legal and compliance: "Find all contracts mentioning indemnification"
- Auditing and review: "Show me every document modified last week"
File search excels when you need to find and read specific documents, or when you need to verify what exists in a collection.
Where File Search Falls Short
File search breaks down when:
- You don't know which document contains the answer
- The answer spans multiple documents
- You need a synthesized response, not a reading list
- You want to ask natural language questions
- The information is buried in long documents
Ask a file search system "What was our revenue growth in APAC last quarter?" and you'll get a list of financial reports. You still have to find the answer yourself.
How RAG Works
RAG (Retrieval Augmented Generation) combines search with AI generation:
- You ask a question in natural language
- The system converts your question into a semantic search
- Relevant chunks of content are retrieved from your documents
- Those chunks are passed to a Large Language Model (LLM) as context
- The LLM generates an answer based on that context
The key insight: RAG doesn't just find documents—it reads them for you and synthesizes an answer.
The RAG Pipeline
A typical RAG system involves several steps:
Ingestion:
- Documents are parsed and converted to text
- Text is split into chunks (paragraphs, sections, or semantic units)
- Each chunk is converted to a vector embedding
- Embeddings are stored in a vector database
Retrieval:
- User question is converted to a vector embedding
- Similar chunks are retrieved based on vector similarity
- Additional filtering may narrow results (by date, source, type)
Generation:
- Retrieved chunks are assembled into a prompt
- An LLM generates a response using the chunks as context
- The response is returned to the user, often with source citations
When RAG Works Well
- Question answering: "What's our refund policy for enterprise customers?"
- Synthesis across sources: "Summarize the key points from this week's customer calls"
- Natural language interaction: Users ask questions like they would ask a colleague
- Getting to answers quickly: No need to read through multiple documents
RAG shines when users need answers, not documents.
Where RAG Falls Short
RAG has its own limitations:
- Hallucination risk: LLMs can generate confident-sounding wrong answers
- Context window limits: Can only consider so much text at once
- Retrieval quality: If the right chunks aren't retrieved, the answer will be wrong
- No document-level view: Hard to get "show me everything about X"
- Verification difficulty: Users may struggle to verify AI-generated answers
The Real Question: What Problem Are You Solving?
The file search vs. RAG debate often misses the point. The real question is: what does your user need?
Often, users need both. They want to ask a question and get an answer (RAG), then drill into the source documents to verify or learn more (file search).
Why "File Search" Products Are Adding RAG
You've probably noticed that file search products are rapidly adding AI features:
- Google's File Search Tool (Gemini API) now includes RAG capabilities
- Microsoft Copilot layers RAG over SharePoint and OneDrive
- Dropbox Dash combines search with AI-powered answers
- Notion AI answers questions about your workspace
This convergence isn't accidental. Users expect to ask questions and get answers—not just find documents. Pure file search feels antiquated once you've experienced good RAG.
But bolting RAG onto file search has limitations. These systems are often:
- Constrained to a single file type or source
- Limited in their retrieval sophistication
- Unable to connect information across sources
- Missing entity extraction and knowledge graphs
Why Pure RAG Isn't Enough Either
On the flip side, pure RAG systems have their own problems:
The "I need to see it" problem: Users often don't trust AI answers. They want to see the source, read the context, and verify for themselves. A system that only gives answers without easy access to source documents frustrates users.
The "show me everything" problem: Sometimes users don't have a specific question—they want to browse and explore. RAG is optimized for questions, not discovery.
The "I know what I'm looking for" problem: If you know the document exists and roughly what it's called, file search is faster than asking questions and hoping the RAG system retrieves the right thing.
The retrieval quality problem: RAG is only as good as its retrieval. If the chunking is wrong, if the embeddings miss semantic nuance, if the wrong content is retrieved—the generated answer will be wrong or incomplete.
The Better Answer: Hybrid Search + Semantic Infrastructure
The most effective systems combine multiple approaches:
1. Hybrid Search
Combine vector similarity search (semantic) with keyword search (BM25/traditional). This catches both conceptual matches and exact term matches.
2. Knowledge Graphs
Extract entities (people, companies, topics, dates) and relationships. This enables queries like "What has Alice said about the Chicago project?" that pure vector search struggles with.
3. Multiple Retrieval Strategies
Use different retrieval approaches for different query types:
- Semantic search for conceptual questions
- Keyword search for specific terms
- Entity-based retrieval for questions about people/companies
- Temporal filtering for time-based queries
4. Source Access
Always provide access to source documents. Let users verify AI answers and explore further.
5. Metadata and Filtering
Combine content search with metadata filtering. "Contracts from 2024" needs both content understanding and metadata awareness.
How Graphlit Approaches This
At Graphlit, we've built infrastructure that supports both file search and RAG—plus the semantic layer that makes both work better.
Ingestion: Documents are parsed, chunked semantically, and embedded. But we also extract entities (people, organizations, places, events) and build a knowledge graph.
Hybrid Search: Every query runs through vector similarity AND keyword matching, with results fused for better relevance.
Entity-Aware Retrieval: Ask about a person, and we retrieve content connected to that entity across all documents—even if their name is spelled differently or referred to by title.
Source Access: Every RAG response links back to source content. Users can drill into the original documents.
Multiple Interfaces: The same knowledge base supports both conversational AI (RAG) and traditional search/filtering (file search).
The result: Users can ask questions and get answers, browse and discover, filter by metadata, and always verify with source documents. The system handles the complexity; users get what they need.
Practical Guidance
If you're building or buying a system for document-based AI, here's what to consider:
Ask these questions:
- What do users actually need? Answers? Documents? Both?
- How will users verify AI responses? Can they easily access sources?
- What types of queries matter? Specific lookups? Open-ended questions? Both?
- How diverse is your content? Single format or many types?
- Do you need entity awareness? Questions about people, companies, projects?
Watch out for:
- RAG-only systems that make it hard to browse or verify
- File search systems that bolt on AI without proper retrieval infrastructure
- Single-source limitations that can't connect information across systems
- Poor chunking that retrieves irrelevant content or misses relevant content
- No entity extraction that misses questions about people and relationships
Prioritize:
- Hybrid search that combines semantic and keyword approaches
- Source attribution that lets users verify and explore
- Entity awareness that understands people, companies, and relationships
- Flexible interfaces that support both questions and browsing
- Quality retrieval that actually finds the right content
Summary
File search and RAG aren't competitors—they're complementary capabilities that serve different needs.
File search helps you navigate to documents. It's essential for discovery, verification, and when you know what you're looking for.
RAG helps you get answers without reading everything yourself. It's essential for question-answering, synthesis, and natural language interaction.
The best systems combine both, plus semantic infrastructure (knowledge graphs, entity extraction, hybrid search) that makes both work better.
When evaluating tools or building systems, don't ask "file search or RAG?"—ask "what do my users need, and does this system actually deliver it?"
The answer is almost always: they need both, working together, backed by solid retrieval infrastructure.
Related Resources:
- Complete Guide to Search — Hybrid search implementation
- Building Knowledge Graphs — Entity extraction and connections
- OpenAI Retrieval vs. Graphlit — Comparison of approaches
- Google File Search vs. Graphlit — When file search isn't enough
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