Learn how RAG works step by step, from document ingestion and embeddings to retrieval, reranking, AI generation, and final answers.
RAG works by retrieving relevant information from an external knowledge source and providing that information to a generative AI model before it creates an answer. A typical RAG pipeline processes documents, creates searchable representations, retrieves relevant content for a user's question, optionally reranks the results, and sends the best context to the AI model for generation.
The process can be summarized as:
Documents → Processing → Chunking → Embeddings → Knowledge Store → Retrieval → Context → AI Model → Answer
But there is much more happening behind that simple flow.
Large language models are powerful at understanding and generating language, but a business application often needs access to information outside the model's general knowledge.
For example, imagine an organization has thousands of technical documents.
An employee asks:
“What is the approved process for restoring a production database?”
The AI model alone may not know the company's internal procedure.
A RAG system can search the organization's knowledge base, retrieve the relevant documentation, and provide it to the model.
The model can then generate an answer based on that retrieved context.
This makes the retrieval pipeline a critical part of the application.
A RAG pipeline can be divided into two major phases:
This happens before users ask questions.
Business Data
↓
Document Processing
↓
Chunking
↓
Embeddings
↓
Knowledge Store
This happens when a user interacts with the application.
User Question
↓
Query Processing
↓
Retrieval
↓
Ranking / Filtering
↓
Relevant Context
↓
AI Model
↓
Final Answer
The first phase prepares the knowledge.
The second phase uses that knowledge.
Every RAG application begins with information.
Depending on the use case, this could include:
The quality of these sources directly influences the quality of the resulting AI system.
If the source material is incomplete or outdated, retrieval cannot magically make it correct.
Raw documents usually need to be processed before they can be used effectively.
A document-processing pipeline may:
For example, a PDF might contain:
Title
Introduction
Product Information
Pricing
Terms
Footer
Page Number
The processing pipeline attempts to distinguish useful content from elements that do not contribute meaningful knowledge.
Large documents are usually divided into smaller pieces called chunks.
Why?
Because sending an entire document to an AI model for every question can be inefficient and may provide too much irrelevant information.
Instead, the system can retrieve only the sections related to the user's question.
For example:
100-page document
↓
Document processing
↓
Section identification
↓
Chunk 1
Chunk 2
Chunk 3
Chunk 4
...
Chunk 500
The goal is to create chunks that are:
Chunking directly affects retrieval quality.
Consider a document containing:
“Enterprise customers receive priority support under the Premium Support Agreement.”
If the chunk is too small, the surrounding conditions might be separated from this statement.
If the chunk is too large, retrieval may return several pages of unrelated information.
Good chunking attempts to preserve the meaning of the original content while keeping retrieval efficient.
Once the documents have been divided into chunks, the system can generate embeddings.
An embedding is a numerical representation of content that allows a system to compare semantic relationships between pieces of text.
For example:
Document:
“Customers can reset their password from the account security page.”
User query:
“I forgot my login password. How can I change it?”
The words are different, but the underlying meaning is closely related.
Semantic embeddings help the retrieval system recognize that relationship.
The document chunks and their embeddings can then be stored in a searchable knowledge system.
A RAG implementation may use:
The stored information may look conceptually like:
| Chunk | Embedding | Metadata |
|---|---|---|
| Password reset instructions | Vector | Support |
| Refund policy | Vector | Billing |
| Product warranty | Vector | Product |
| Enterprise SLA | Vector | Enterprise |
Metadata can provide additional filtering and control.
Now the actual RAG interaction begins.
Suppose the user asks:
“What is the refund period for an enterprise subscription?”
The application receives this query.
It now needs to determine which pieces of the knowledge base are relevant.
The user's question may be transformed or enriched before retrieval.
The application may consider:
For example, the system might know that the user is asking about a specific enterprise product.
That context can improve retrieval.
The system searches the knowledge store for relevant content.
Suppose it finds:
Result 1:
Enterprise Subscription Refund Policy
Result 2:
General Customer Refund Policy
Result 3:
Enterprise Subscription Terms
Result 4:
Billing FAQ
The retrieval layer may return several candidate passages rather than immediately selecting one.
Vector search finds content based on semantic similarity rather than relying only on exact keyword matches.
Traditional keyword search might look for:
“enterprise refund period”
Vector search can potentially identify related concepts even when the wording differs.
For example:
“How long do enterprise customers have to request their money back?”
can still retrieve a document containing:
“Enterprise subscribers may request a refund within the applicable refund window.”
This is one reason semantic retrieval is useful for conversational AI.
Hybrid search combines different retrieval approaches.
For example:
Keyword Search
+
Semantic Search
↓
Combined Results
Keyword search can be valuable for exact terms such as:
Semantic search is useful when users express concepts in different ways.
Combining the two can provide stronger retrieval for many business applications.
Not every retrieved result should necessarily reach the language model.
The system may filter results using metadata such as:
For example, an employee may be allowed to retrieve HR policies but not confidential executive documents.
This means enterprise RAG is not only a search problem.
It is also an access-control problem.
Some RAG systems add a re-ranking stage.
The initial retrieval might produce ten potentially relevant results.
A re-ranker can examine those candidates and determine which ones are most relevant to the user's specific question.
Conceptually:
Initial Search
↓
10 Candidate Results
↓
Re-Ranking
↓
Top 3 Relevant Results
This can reduce irrelevant context before the information reaches the language model.
The application now prepares the information that will be sent to the AI model.
For example:
User Question:
What is the refund period for enterprise subscriptions?
Retrieved Context:
Enterprise customers may request refunds within the specified refund period under the Enterprise Subscription Agreement.
Instruction:
Answer using the provided information.
This creates a grounded prompt for the model.
The language model receives:
The model then generates the answer.
The important point is that the model has been given specific external context related to the question.
The AI model transforms the retrieved information into a natural-language response.
For example:
“Enterprise subscriptions are eligible for refunds within the applicable refund period specified in the Enterprise Subscription Agreement.”
A production system might also provide a citation or source reference.
More advanced RAG systems may introduce validation before returning the response.
The system can check:
This additional layer can improve reliability.
Finally, the application displays the response.
The user sees a simple conversational answer.
Behind that answer, however, the system may have completed an entire retrieval pipeline.
User
↓
Question
↓
Query Processing
↓
Search
↓
Filtering
↓
Re-Ranking
↓
Context
↓
LLM
↓
Validation
↓
Answer
That is the basic journey of a RAG request.
A well-designed RAG system should know how to handle missing information.
If no relevant content is retrieved, the system should not automatically invent an answer.
Instead, it could respond with something like:
“I couldn't find this information in the available documentation.”
Depending on the application, it could then:
Knowing when not to answer is an important part of reliable AI design.
RAG can help reduce hallucinations by providing the model with relevant external context.
Instead of asking:
“What is our company's cancellation policy?”
with no additional information, the application can retrieve the actual policy and ask the model to answer using that content.
However, RAG does not guarantee hallucination-free AI.
Problems can still occur if:
Reliable RAG therefore requires more than retrieval alone.
Several factors influence retrieval quality.
Better source material produces better knowledge.
Meaningful chunks preserve context.
Good embeddings improve semantic matching.
Vector, keyword, hybrid, and filtered retrieval can be combined depending on the use case.
Metadata helps narrow results to the correct context.
Re-ranking can improve the ordering of candidate results.
A well-processed query can improve what the system retrieves.
RAG does not have to operate only on PDFs or text documents.
A business can combine retrieval with structured data.
For example:
Natural Language Question
↓
AI Application
↓
Knowledge Base
+
Business API
↓
AI Model
↓
Answer
A customer-support application could retrieve product documentation while simultaneously accessing approved real-time account information through an API.
This creates a much more useful AI application.
RAG provides knowledge.
AI agents can use that knowledge while performing actions.
For example:
“Check the return policy and process my eligible return.”
A RAG system could retrieve the return policy.
An AI agent could then:
This is where RAG becomes a component of a larger agentic architecture.
A search engine primarily returns information.
A RAG system retrieves information and gives it to a generative AI model that can synthesize an answer.
Question
↓
Search
↓
Documents
↓
User reads
Question
↓
Retrieval
↓
Relevant information
↓
AI model
↓
Generated answer
RAG therefore adds a generation layer on top of retrieval.
A RAG system can fail at several stages.
The required information was never added to the knowledge base.
Important information was lost during document extraction.
Related information was separated incorrectly.
The correct chunk was not found.
Relevant information was pushed below irrelevant results.
Too much or too little information was provided to the model.
The model misunderstood or incorrectly used the context.
This is why evaluating only the final answer is not enough.
A production RAG system should evaluate the entire pipeline.
Businesses should measure both retrieval and generation quality.
Useful metrics can include:
A useful testing process involves real questions rather than only synthetic examples.
The evaluation dataset should include:
A production system may contain considerably more than a basic vector search setup.
Data Sources
↓
Data Ingestion
↓
Processing + Extraction
↓
Chunking + Metadata
↓
Embeddings
↓
Knowledge Store
↓
User Query
↓
Retrieval
↓
Filtering
↓
Re-Ranking
↓
Context Builder
↓
AI Model
↓
Guardrails / Validation
↓
Final Answer
↓
User
Additional components can include:
The architecture should be designed around the application's requirements rather than simply adding technologies for their own sake.
RAG is particularly useful when a business has valuable information that people frequently need to search, understand, or summarize.
Good candidates include:
The strongest use cases usually have a clear business problem, such as employees spending significant time searching through documents.
RAG is not automatically the answer to every AI problem.
You may not need RAG when:
The architecture should follow the problem.
It is also important to distinguish RAG from normal database querying.
A database is excellent for questions such as:
“How many orders were placed today?”
A RAG system is more suitable for questions such as:
“What does our enterprise return policy say about damaged products?”
A hybrid system can combine both.
Natural Language Question
↓
Query Router
↙ ↘
Database RAG
↓ ↓
Structured Documents
Data / Knowledge
↘ ↙
AI Response
This architecture can allow an AI application to work with both structured and unstructured information.
Building a reliable RAG application requires both AI and software engineering.
A development team can help with:
MYST International's technology capabilities span AI, custom software, web and mobile application development, and technology consulting, making RAG suitable as part of a larger business application rather than an isolated AI experiment.
The first practical step is identifying and preparing the knowledge sources the AI application needs. Documents, databases, websites, or other approved sources are processed before they can be indexed and retrieved.
RAG can use semantic vector search, keyword search, hybrid retrieval, metadata filtering, or combinations of these techniques to identify information relevant to a user's question.
Documents are divided into chunks so the retrieval system can identify and provide focused pieces of information instead of passing entire documents to the AI model.
No. A RAG system does not inherently require a specific type of database. Vector search is common, but RAG architectures can use different retrieval technologies depending on the application's requirements.
Yes, if the application is designed to retrieve information from live or frequently updated sources such as APIs, databases, or other connected systems.
No. RAG retrieves external information at runtime, while fine-tuning changes model behavior through additional training. They solve different problems and can sometimes be combined.
RAG is best understood as a journey of information.
A user asks a question.
The application understands the request.
The retrieval system searches the available knowledge.
Relevant information is selected.
The context is passed to the AI model.
The model generates an answer.
And, in a production system, validation and access controls help ensure that the answer is appropriate.
That entire process is what makes Retrieval-Augmented Generation more than simply “AI connected to documents.”
For businesses, the real opportunity is to turn existing knowledge into an intelligent interface—one that can help employees, customers, developers, and teams find and understand information faster.
And when RAG is combined with APIs, business systems, and AI agents, it can become the knowledge layer behind much more powerful automated workflows.
The first practical step is identifying and preparing the knowledge sources the AI application needs. Documents, databases, websites, or other approved sources are processed before they can be indexed and retrieved.
RAG can use semantic vector search, keyword search, hybrid retrieval, metadata filtering, or combinations of these techniques to identify information relevant to a user's question.
Documents are divided into chunks so the retrieval system can identify and provide focused pieces of information instead of passing entire documents to the AI model.
No. A RAG system does not inherently require a specific type of database. Vector search is common, but RAG architectures can use different retrieval technologies depending on the application's requirements.
Yes, if the application is designed to retrieve information from live or frequently updated sources such as APIs, databases, or other connected systems.
No. RAG retrieves external information at runtime, while fine-tuning changes model behavior through additional training. They solve different problems and can sometimes be combined.