Understanding RAG
Learn how Retrieval-Augmented Generation retrieves the right context, builds a prompt, and helps an AI answer using external knowledge.
What RAG actually solves
A language model is powerful, but it does not automatically know your private docs, support tickets, latest product changes, or internal policies. RAG gives it the relevant context at answer time.
Private knowledge
Your company wiki, APIs, HR policies, and customer docs are not inside the model by default.
Fresh knowledge
RAG can use updated documents without retraining the model every time something changes.
Grounded answers
The answer can be tied back to retrieved chunks instead of relying only on model memory.
What you will learn
Interactive RAG Pipeline Sandbox
Client-side demo with predefined chunks and deterministic retrieval scores.
Run the pipeline
Choose a knowledge base and a question, then watch retrieval, filtering, prompt assembly, and the final demo answer.
What happened?
Selected chunks
0 includedRejected chunks
0 rejectedPrompt sent to the LLM
Generated demo answer
The RAG pipeline, step by step
RAG is easier to understand when you separate it into two phases: preparing searchable knowledge, and answering a user question.
Phase 1: ingestion
- Collect documents from PDFs, docs, tickets, code, or product pages.
- Clean the text and remove duplicate or useless content.
- Split documents into focused chunks.
- Create embeddings for each chunk.
- Store chunk text, vectors, and metadata.
Phase 2: answering
- Convert the user question into a query embedding.
- Search for nearby document vectors.
- Apply filters, Top K, thresholds, and optionally reranking.
- Build a prompt with the retrieved context.
- Ask the LLM to answer using that context.
Chunking explained
Chunking is where many RAG systems quietly succeed or fail. Good chunks are focused enough to retrieve, but complete enough to answer.
Too small
Bad: “It is allowed twice per week.” The chunk lost the subject, so retrieval and generation both suffer.
Too large
Bad: a whole policy page with vacation, parking, sick leave, expense rules, and office hours mixed together.
Good chunk
Better: “Remote work is allowed up to two days per week with manager approval.” Focused and complete.
Retrieval knobs that matter
Top K
The maximum number of chunks returned. Too low can miss context. Too high can add noise and waste tokens.
Threshold
The minimum similarity score required. Too strict returns nothing. Too loose sends irrelevant chunks to the model.
Prompt budget
The amount of retrieved text allowed into the prompt. Bigger is not always better because irrelevant context can confuse the answer.
If vector similarity is unclear, start with the Vector Similarity tutorial first.
RAG vs fine-tuning vs long context
Common RAG failure modes
Wrong chunks retrieved
The answer fails before generation even starts because the model receives the wrong evidence.
No relevant chunks
Thresholds or bad embeddings may filter out the only useful context.
Chunks too noisy
Large or messy chunks can contain the answer but also a lot of distracting text.
No source visibility
Users cannot verify whether the answer came from real retrieved content.
Outdated documents
The retrieval system may be correct, but the indexed content is stale.
No evaluation
If you never measure retrieval quality, you will debug generated answers blindly.
Production RAG checklist
Data
- Define sources
- Clean documents
- Attach metadata
- Handle stale content
Retrieval
- Choose chunk size
- Select embeddings
- Tune Top K
- Use metadata filters
Quality
- Add source citations
- Evaluate Recall@K
- Monitor bad answers
- Collect feedback
RAG FAQ
Short answers to the questions developers usually hit when moving from “RAG sounds simple” to “why is my retrieval bad?”
Is RAG the same as fine-tuning?
No. RAG retrieves knowledge at runtime. Fine-tuning changes model behavior or patterns. They solve different problems.
Does RAG stop hallucinations completely?
No. It reduces hallucination risk when retrieval is good and the prompt forces grounding, but bad retrieval can still cause bad answers.
Do I always need a vector database?
No. For tiny datasets you can start simpler. A vector database becomes useful when you need scalable semantic search.
What is Top K?
Top K is how many matching chunks are returned. Top K = 3 means “return the 3 best chunks.”
What happens if Top K is too high?
The prompt may include weak or irrelevant chunks, which can increase cost and confuse the model.
What happens if Top K is too low?
The system may miss supporting context, especially when the answer needs multiple pieces of evidence.
What is a similarity threshold?
A cutoff score. Chunks below the threshold are rejected even if they are in the top results.
Why split documents into chunks?
RAG retrieves focused pieces of text. Whole documents are often too large and too noisy.
What is chunk overlap?
Overlap repeats a small part of neighboring chunks so important context is not lost at chunk boundaries.
What is reranking?
Reranking takes initial search results and reorders them with a slower but more accurate scoring step.
Can RAG work with private documents?
Yes. That is one of its strongest use cases, assuming access control and data privacy are handled correctly.
Can RAG use fresh data?
Yes. You update the index when documents change instead of retraining the model.
Can RAG work without embeddings?
Yes, you can use keyword or hybrid search. Embeddings are common because they capture semantic similarity.
How do I debug a bad RAG answer?
First inspect retrieved chunks. If the right chunk is missing, fix retrieval before blaming the LLM.
What is grounding?
Grounding means forcing the answer to be based on provided sources instead of model memory.
When should I not use RAG?
When you need pure writing style, fixed classification behavior, or a tiny static knowledge base that fits easily in one prompt.
Glossary
Retrieval
Finding relevant information before generation. Example: search the HR policy before answering a vacation question.
Chunk
A small piece of a larger document. Example: one paragraph from a product manual.
Embedding
A vector representation of text. Example: turning “password reset” into a list of numbers.
Vector database
A database optimized for storing vectors and finding nearest neighbors.
Query vector
The vector created from the user question.
Document vector
The vector created from a stored chunk or document.
Semantic search
Search by meaning, not just exact words.
Hybrid search
Combining keyword search and vector search.
Prompt assembly
Building the final prompt from instructions, retrieved chunks, and the user question.
Source citation
Showing which source chunk supported the answer.
Learn more
A small curated list of useful resources. This page is not a replacement for production documentation.
Embeddings Guide
Useful for understanding text embeddings and similarity use cases.
Embeddings
A beginner-friendly explanation of embeddings and vector representations.
RAG Guide
Practical vector database and RAG concepts.
Vector Search
Good conceptual documentation about vector search.
Search Concepts
Useful docs about vector search and filtering.
RAG Tutorial
Implementation-oriented guide for building RAG flows.