The Hallucination Problem: Why Models Make Up Facts
Language models trained on 2025 data can't answer questions about 2026 events. They hallucinate (confidently make up answers). Internal company data isn't in training data, so models confabulate rather than admit ignorance. A customer asks about your product roadmap, the model generates plausible-sounding but completely fabricated features.
Retrieval-Augmented Generation (RAG) solves this. Instead of relying solely on training data, RAG retrieves relevant information from your knowledge base, grounding the model's responses in real data. The model becomes a "knowledge assistant" using your information rather than trying to remember what it learned during training.
How RAG Works
The Ingestion Phase
Your documents (PDFs, web pages, databases, internal notes) are processed and converted into numerical representations (embeddings). These embeddings capture semantic meaning: the information in the document is distilled into a vector format. Embeddings are stored in a vector database for fast retrieval.
Example: a 1000-page customer support documentation becomes 10,000 document chunks, each converted to an embedding. These embeddings enable finding relevant documentation instantly when questions arrive.
The Query Phase
When a user asks a question, the system converts the question into an embedding. It searches the vector database for documents with similar embeddings (semantically relevant documents). The top-N relevant documents are retrieved.
Example: customer asks "How do I reset my password?" The system finds relevant documentation: "Password Reset Procedure," "Account Recovery," "Security FAQs." These documents ground the response.
The Generation Phase
Retrieved documents are combined with the user query in a prompt. The language model reads both the retrieved context and the question, then generates an answer grounded in the context. Since the model is working with real information, hallucination is eliminated.
Example: "Here's the customer documentation [insert relevant docs]. Based on this, answer the user's question: How do I reset my password?" The model answers using the documentation.
| RAG Component | Purpose | Technology Examples |
|---|---|---|
| Document Processing | Convert documents to chunks, create embeddings | LangChain, LlamaIndex, Unstructured |
| Vector Database | Store and search embeddings | Pinecone, Weaviate, Milvus, Qdrant |
| Retrieval | Find relevant documents | Semantic search, hybrid search |
| Generation | Generate answer from context and query | GPT, Claude, local LLMs |
RAG Implementation Challenges and Solutions
Chunking Decisions
How to split documents into chunks? Naive approaches: split at fixed token boundaries (might break sentences). Better approaches: split at semantic boundaries (paragraph breaks, section headers). Ideal: use ML models to identify meaningful chunk boundaries.
Retrieval Quality
Vector similarity search sometimes retrieves irrelevant documents. A question about "payment" might retrieve documents about "holiday" (similar word vectors). Solutions: hybrid search (combine vector search with keyword search), semantic ranking (re-rank retrieved documents for relevance), and metadata filtering (only search relevant document categories).
Context Length Limits
Models have context windows (max tokens they can process). A very long question plus 5 very long retrieved documents might exceed limits. Solutions: rerank retrieved documents to keep only the most relevant, use summarization to compress context, or chunk questions into multiple queries.
Keeping Information Current
Once ingested, documents become static. Company policies change, products evolve, new information emerges. Solutions: schedule regular re-ingestion of documents, implement document versioning to track changes, monitor what questions the system can't answer and manually add missing information.
Building Your RAG System
Step 1: Identify Your Knowledge Sources
What information should ground your assistant? Internal documentation, product guides, customer data, research papers, company policies. Assemble all relevant sources.
Step 2: Prepare and Ingest Documents
Extract text from PDFs, web pages, databases. Clean and normalize. Split into optimal-sized chunks. Generate embeddings and store in vector database.
Step 3: Set Up Retrieval
Implement semantic search. Consider hybrid search (vector plus keyword). Test retrieval quality. Adjust chunk size and search parameters based on actual queries.
Step 4: Connect to LLM
When users query, retrieve relevant documents, combine with query in a prompt, send to LLM. Generate response grounded in retrieved context.
Step 5: Evaluate and Improve
Test on real use cases. Measure: accuracy (do answers match knowledge source), relevance (are retrieved documents actually helpful), and coverage (does the system answer questions it should). Iterate based on failures.
Real-World RAG Applications
Customer support: company documentation becomes knowledge base. Customer questions retrieve relevant docs, LLM crafts personalized answers. Response quality and consistency improve dramatically. Human specialists handle edge cases.
Enterprise assistants: company policies, procedures, and historical decisions become knowledge base. Employees ask questions, get answers grounded in official company information. Reduces confusion and ensures consistency.
Research assistants: scientific papers become knowledge base. Researchers ask questions, get answers synthesizing relevant research. Accelerates literature review and idea development.