Hallucinations
LLMs confidently generate false information based on training patterns.
Citing non-existent research papers or making up statistics.
Guide 01 — Fundamentals
What is RAG?
Retrieval-Augmented Generation combines a large language model with retrieval from your own documents, so answers are grounded in real sources instead of the model's memory.
01 — The problem
Language models answer from memory.
Stale Knowledge
LLM training has a cutoff date, missing recent information.
Unable to answer questions about events after training.
No Source Attribution
There is no way to trace where information came from.
Users have no way to verify claims made by the model.
02 — The core insight
Look it up first, then answer.
Instead of relying only on what an LLM learned during training, RAG retrieves relevant passages from your own documents and hands them to the model as context for its answer.
That grounds responses in actual data, reduces hallucination, and opens up private or recent information the model never saw.
# Pseudocode
def rag_query(question, documents):
# 1. Find relevant context
context = retrieve(question, documents)
# 2. Augment the prompt with context
prompt = build_prompt(question, context)
# 3. Generate a grounded answer
return llm(prompt)03 — Step by step
The RAG pipeline, step by step
Document Ingestion
Documents are loaded and split into smaller chunks for processing.
- Split documents into overlapping chunks
- Preserve context at boundaries
- Handle multiple file formats (PDF, TXT)
Embedding Generation
Each chunk is converted into a vector embedding.
- Use embedding models (e.g., text-embedding-3-small)
- Capture semantic meaning in vectors
- Enable similarity comparisons
Vector Storage
Embeddings are stored in a vector database for fast retrieval.
- Index vectors for similarity search
- Use FAISS for efficient storage
- Support approximate nearest neighbors
Query Processing
User questions are embedded using the same model.
- Convert question to vector
- Match vector space with documents
- Prepare for similarity search
Context Retrieval
Most similar document chunks are retrieved from the vector store.
- Find top-k most similar chunks
- Rank by cosine similarity
- Return with similarity scores
Augmented Generation
The LLM generates an answer using the retrieved context.
- Inject context into prompt
- Generate grounded response
- Cite sources in answer
04 — In production
Why teams ship RAG.
Use cases
- Customer support chatbots with company documentation
- Internal knowledge bases for employees
- Legal document search and analysis
- Medical literature review systems
- Code documentation assistants
Benefits
- Answers grounded in real documents
- Access to private and proprietary data
- Up-to-date information beyond the training cutoff
- Auditable responses with source citations
- Lower cost than fine-tuning models
Next
See every component up close.
Explore the architecture step by step, or skip ahead and try the pipeline on your own document.