What Is Retrieval-Augmented Generation (RAG)?
Introduction
Have you ever asked an AI a question about a company document, a product manual, or information that was recently updated?
A general AI model may not have access to that specific information. Even when the information exists somewhere online or inside a private database, the model may not be able to use it when answering your question.
This is where Retrieval-Augmented Generation (RAG) comes in.
RAG allows an AI system to search external information and provide relevant content to the AI model before it generates an answer. Instead of relying only on information available to the model, RAG can connect it to documents, databases, knowledge bases, and other information sources.
In this guide, you will learn what RAG is, how it searches for information, how retrieved information becomes context, how RAG can provide sources, and how it differs from Fine-Tuning, search engines, and AI Agents.
We will also look at real-world uses of RAG and its limitations, so you can understand not only what RAG can do, but also when it may not work well.
What Is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a method that allows an AI system to retrieve relevant information from external sources and use that information when generating an answer.
Instead of relying only on the information available to the language model, a RAG system first searches a connected knowledge source, such as documents, databases, or a company knowledge base. It then provides the relevant information to the language model as context for generating the response.
In simple terms, RAG connects an AI model to external information so it can use relevant information when answering questions.
Why Was RAG Created?
RAG was developed to address an important limitation of language models: they may not have access to specific or recently updated information.
For example, a model may not have access to a company's private documents or information that changed after its training.
RAG provides a way to connect the model to external information without retraining the model every time that information changes.
Does RAG Change the LLM?
RAG does not normally change the language model's parameters.
Instead, RAG works as a system around the language model. It retrieves relevant information from an external source and adds that information to the context provided to the model.
The model can then use this additional context when generating its answer.
This means that RAG can give a language model access to additional information without changing the model's internal parameters.
When Is RAG Useful?
RAG is especially useful when an AI system needs to work with information that is private, specific, or frequently updated.
For example, a company may want an AI assistant to answer questions using its internal documents. A product team may need an AI system to provide the latest product information. A person may also want to ask questions about their own PDFs or notes.
In these situations, the key question is:
How can we connect an existing AI model to new or specific information?
Company Internal Documents
A company may have information that is not part of the data used to train a public AI model, such as internal policies, employee guides, product documents, or other private files.
RAG can retrieve relevant information from these documents and provide it to the AI model when answering a question.
For example, an employee could ask, “What is our remote work policy?” The system can search the company's internal documents and use the relevant information to generate an answer.
Frequently Updated Product Information
RAG can also be useful when information changes frequently.
Product prices, features, availability, and documentation may change over time. Instead of relying on information stored in a model's training, a RAG system can retrieve the latest information from a connected data source.
When the source is updated, the system can retrieve the new information when answering future questions.
Information from Specific Websites or Databases
Some AI applications need to work with information from a particular website or database.
For example, a business could connect an AI assistant to its product database so customers can ask questions about current products or inventory.
RAG can act as a bridge between the information source and the AI model, retrieving relevant data and providing it as context for the answer.
Personal PDFs and Documents
RAG can be useful when you want an AI system to work with your own documents, such as PDFs, manuals, reports, or notes.
Instead of using an entire collection of documents for every question, a RAG system can search the available documents and retrieve the parts that are most relevant to the question.
For example, you could ask a question about a long report, and the system could find the relevant sections before generating an answer.
Specialized Knowledge Bases
RAG is also useful for applications that need to work with specialized knowledge.
A company might build a knowledge base containing technical manuals, product documentation, policies, or other authoritative materials. A RAG system can retrieve relevant information from this knowledge base and provide it to the AI model.
This allows a general-purpose AI model to work with specific knowledge without requiring that knowledge to be built directly into the model through additional training.
Overall, RAG is most useful when an AI system needs access to private, specialized, or frequently updated information.
How Does RAG Work?
 |
| Figure 1. How an AI system retrieves and uses external information to generate an answer |
A RAG system generally follows four basic steps:
User Question → Retrieve Information → Add Context → Generate Answer
Here is what happens at each step.
1. User Asks a Question
The process starts when a user asks a question.
For example:
“What is our company's remote work policy?”
The RAG system receives the question and uses it to search the connected knowledge source.
2. Retrieve Relevant Information
The system searches its connected knowledge source for information related to the question.
Depending on how the RAG system is designed, it may use keyword search, semantic search, vector search, or a combination of these methods.
The goal is to find the information that is most relevant to the user's question, rather than sending every available document to the AI model.
3. Add the Retrieved Information as Context
The relevant information found during the search is then provided to the AI model as context.
The model receives both the user's question and the retrieved information.
You can think of this like an open-book test. Instead of answering only from what it already knows, the AI model is given relevant material to refer to before generating its answer.
4. Generate the Answer
Finally, the language model uses the user's question and the retrieved context to generate an answer.
For example, if the retrieved company policy says that employees can work remotely two days a week, the AI can use that information when answering the question.
The quality of the final answer depends partly on the quality of the information that was retrieved. RAG can help ground an AI response in relevant external information, but it does not guarantee that every answer will be correct.
How Are Documents Prepared for RAG?
Before a RAG system can retrieve information, the documents it uses need to be prepared for efficient searching.
One important step is called chunking.
What Is Chunking?
Chunking means breaking a large document into smaller, meaningful sections called chunks.
For example, instead of treating a 100-page PDF as one large piece of text, a RAG system can divide it into smaller sections. Each section can contain a few paragraphs or a specific part of the document.
This makes it easier for the system to find the information that is relevant to a user's question.
Why Not Use the Entire Document?
Sending an entire large document to the AI model for every question can be inefficient. A document may contain a lot of information that is unrelated to the user's question, making it harder for the model to focus on the most relevant content.
By dividing the document into smaller chunks, a RAG system can retrieve only the parts that are most relevant to the user's question. This helps provide the model with more focused context instead of the entire document.
How Are Chunks Stored?
After a document is divided into chunks, each chunk can be stored along with additional information called metadata.
Metadata can include details such as:
- The document name
- The page number
- The section or title
- The source
- The date or category
The text and its metadata are stored together so that the system can retrieve the relevant content along with useful information about where it came from.
Think of it like a package: the text is the item inside, while the metadata is the label on the outside. They help the system identify and organize the information when it is retrieved.
In simple terms, chunking turns large documents into smaller, searchable pieces so a RAG system can find and use the right information more efficiently.
Why Are Embeddings Important in RAG?
After documents are divided into smaller chunks, a RAG system needs a way to find the chunks that are most relevant to a user's question.
This is where embeddings become important.
What Is an Embedding?
An embedding is a numerical representation of text that captures information about its meaning and context.
Words, sentences, or document chunks can be converted into numerical vectors. These vectors allow a computer system to compare pieces of text mathematically.
You can think of an embedding as placing text on a large map of meaning. Text with similar meanings tends to be placed closer together on this map.
Why Turn Text Into Numbers?
Computers can perform mathematical calculations on numerical data. By representing text as vectors, an AI system can compare different pieces of text based on their relationships in the vector space.
This allows a RAG system to search for information based on meaning, not just exact words.
How Can RAG Find Similar Meaning?
Consider these two phrases:
“How can I work from home?”
and
“Remote work policy”
They use different words, but they are related to the same topic.
With semantic search, their embeddings can be compared to determine how closely their meanings are related. This allows the system to find relevant information even when the user's question does not use exactly the same words as the document.
When a user asks a question, the RAG system can create an embedding for the question and compare it with the embeddings of stored document chunks. The system then uses this similarity to identify potentially relevant chunks and retrieve them for the next step.
In simple terms, embeddings help RAG find the right information by comparing meaning rather than relying only on matching words.
What Is a Vector Database?
A vector database is a type of database designed to store and search numerical representations of information called vectors.
In RAG, these vectors are usually embeddings created from document chunks. The vector database stores those embeddings so the RAG system can search for information that is relevant to a user's question.
A traditional database often searches for exact values or matching keywords. A vector database can search for information based on similarity in meaning.
Why Does RAG Need a Vector Database?
A RAG system may need to search through a large number of document chunks.
Instead of checking every document from the beginning each time a user asks a question, the system can store the embeddings of those chunks in a vector database.
When a user asks a question, the question can also be converted into an embedding. The system then compares it with the stored embeddings and identifies document chunks that are most similar to the question.
For example, a user might ask:
“Can employees work from home?”
A document might contain:
“Employees are allowed to work remotely up to two days per week.”
The wording is different, but the two pieces of text are related in meaning. A vector database can help the RAG system find this relevant information.
What Does a Vector Database Store?
In a RAG system, a vector database can store information such as:
- Embeddings — numerical representations of document chunks
- Document chunks — the original text or a reference to it
- Metadata — information such as document name, page number, section, or source
This allows the system to find relevant information and keep track of where that information came from.
In simple terms, a vector database gives RAG a place to store and search the numerical representations of information used during retrieval.
How Does It Fit Into RAG?
Now that we have seen what embeddings and vector databases do, we can put them together in the RAG process.
A simplified RAG process looks like this:
Documents → Chunks → Embeddings → Vector Database
When a user asks a question, the system uses the same process to find relevant information:
Question → Embedding → Search Vector Database → Retrieve Relevant Chunks → LLM → Answer
The vector database is therefore an important part of the retrieval stage. It stores the document embeddings and helps the system find information that is relevant to the user's question.
In simple terms, embeddings represent the information, while the vector database stores and searches those representations to help RAG retrieve relevant content.
How Does RAG Search for Information?
The retrieval step is one of the most important parts of a RAG system. Before an AI model can generate an answer, RAG first needs to find relevant information from its connected knowledge sources.
There are several ways to search for information. The main approaches are keyword search, semantic search, vector search, and hybrid search.
Keyword Search
Keyword search looks for exact words or phrases in a document.
It works much like using Ctrl + F on a computer. For example, if you search for “remote work policy,” the system looks for documents containing those words.
Keyword search is useful when exact terms matter, such as product names, document titles, codes, or specific phrases.
However, it can miss relevant information when the same idea is expressed using different words.
Semantic Search
Semantic search looks beyond exact words and tries to find information with a similar meaning or context.
For example, a user might ask, “Can I work from home?” while a company document uses the phrase “remote work policy.” A semantic search system can recognize that these two expressions are related and retrieve the relevant section.
This makes semantic search especially useful for natural-language questions.
Vector Search
Vector search is a common technical method used to perform semantic searches.
As explained earlier, text can be converted into embeddings, which represent information as numerical vectors. Vector search compares the vector for a user's question with the vectors stored in the knowledge base and finds information that is mathematically similar.
In simple terms:
Question → Embedding → Compare with stored vectors → Retrieve relevant information
This allows RAG to find relevant information even when the exact words in the question do not appear in the source document.
Hybrid Search
Sometimes keyword search and vector search each find useful information that the other might miss.
Hybrid search combines both approaches. It looks for exact keyword matches while also searching for information based on semantic similarity. The results can then be combined to improve retrieval.
For example, a question containing a specific product name may benefit from keyword search, while the rest of the question may benefit from semantic search.
Why Is Search Important in RAG?
RAG can only use the information that it successfully retrieves and provides to the AI model.
If the system retrieves relevant information, the model has better context for generating an answer. If it retrieves irrelevant or incomplete information, the final answer may also be less useful.
This is why retrieval quality is a key part of a RAG system.
The goal is not simply to find information. The goal is to find the right information for the user's question.
In practice, RAG systems may use different retrieval methods depending on the type of information they need to find. Keyword search, semantic/vector search, and hybrid search each have different strengths.
How Does RAG Provide Evidence for Its Answers?
 |
| Figure 2. How retrieved information can be connected to an AI-generated answer |
One useful feature of RAG is that it can connect an AI-generated answer to the information used to produce it.
This happens because the information retrieved during the search step is passed to the AI model as context.
How Retrieved Information Becomes Context
After the retrieval step finds relevant document chunks, those chunks are passed to the AI model along with the user's question.
The model can then use the retrieved information as additional context when generating its answer.
You can think of this like an open-book test. Instead of answering only from what it already knows, the AI can refer to the relevant information provided by the RAG system.
The basic flow is:
User Question → Search → Relevant Documents → Context → AI-Generated Answer
This can help the answer stay connected to the information found in the source documents.
How Can RAG Show Sources and Citations?
A RAG application can also show users where the retrieved information came from.
For example, a system might display:
- Document name
- Page number
- Section title
- Source link
- Other useful source information
This is possible because information about each document, known as metadata, can be stored together with the text chunks during the RAG process.
For example, a chunk might contain information from a company policy document along with metadata such as its document name and page number. When that chunk is retrieved, the application can use this information to show the source to the user.
There are different ways to build citation features. Some systems ask the AI model to generate citations, while others use the stored metadata and application code to display the sources directly.
The second approach can provide more predictable source links because the application does not rely entirely on the AI model to create them.
Can Users Always Trust RAG Citations?
No. RAG does not guarantee that every citation or source is correct.
A RAG system has several steps, including document preparation, retrieval, context construction, answer generation, and citation handling. An error at any stage can affect the final result.
For example, a system might:
- Retrieve the wrong document
- Provide incomplete context to the model
- Associate a statement with the wrong source
- Generate an incorrect citation
- Fail to display a source
This means that seeing a citation does not automatically prove that an AI answer is correct.
RAG can make answers more traceable and easier to verify, but users should still check important information against the original source when accuracy matters.
This is one reason source citations are valuable: they give users a way to check the information behind an AI-generated answer instead of simply accepting the answer at face value.
RAG vs. Fine-Tuning: What's the Difference?
When you want an AI system to work with new information, you may come across two approaches: RAG and Fine-Tuning.
They solve different problems.
The simplest way to think about the difference is:
RAG gives an AI model information to refer to when answering a question. Fine-Tuning changes how the model behaves or responds.
Adding New Knowledge
If your goal is to give an AI access to new or specific information, RAG can be a practical approach.
For example, a company may want an AI assistant to answer questions using its internal employee handbook. Instead of changing the model itself, RAG can retrieve relevant sections from the handbook and provide them to the model as context.
Fine-Tuning works differently. It uses training examples to adjust the model so that it learns a desired pattern of behavior or output.
Updating Information
RAG is especially useful when information changes frequently.
For example, product prices, company policies, inventory information, or technical documentation may change over time. With RAG, the connected documents or knowledge base can be updated without retraining the underlying model.
Fine-Tuning is generally less convenient when the main goal is to keep changing factual information up to date.
Using Specific Documents
Suppose you want an AI assistant to answer questions using a collection of internal documents, such as training manuals, product documentation, or company policies.
RAG is well suited to this task.
It can retrieve the relevant sections when a user asks a question and provide those sections to the model as context.
Fine-Tuning is not primarily a document-search method. Its main purpose is to change how a model responds based on examples.
Changing Behavior, Style, or Output Format
The choice can be different when you want to change how the AI responds.
For example, you might want a model to:
- Follow a particular writing style
- Consistently use a specific output format
- Respond in a particular tone
- Perform a task in a consistent way
Fine-Tuning can be useful for these kinds of behavior or output changes.
RAG, on the other hand, mainly provides the model with additional information to use when answering. It does not permanently change the model's underlying behavior.
Cost and Maintenance
RAG and Fine-Tuning also differ in how they are maintained.
With RAG, information can often be updated by changing the connected documents or knowledge base. This can make it easier to maintain when the underlying information changes frequently.
Fine-Tuning requires creating and managing training data and producing a new fine-tuned model when changes to the learned behavior are needed.
The actual cost depends on the model, data, infrastructure, and scale, so it is too broad to say that one approach is always cheaper.
RAG or Fine-Tuning: Which Should You Choose?
A simple rule is:
Use RAG when you want an AI system to access specific, private, or frequently changing information.
Consider Fine-Tuning when you want to change the model's behavior, style, or output patterns.
In some real-world systems, the two approaches can also be used together. RAG can provide up-to-date information, while Fine-Tuning can help the model follow a particular way of responding.
The right choice depends on what you are actually trying to change: the information the AI can access, or the way the AI behaves.
Real-World Uses of RAG
RAG can be used in many AI applications that need to work with information from specific documents, databases, or knowledge sources.
Here are some practical examples of how RAG can be used in real-world applications.
Company Internal Knowledge
An employee may ask an AI assistant a question about a company policy, such as:
“How many days of remote work are allowed each week?”
Instead of relying only on general AI knowledge, the system can search the company's internal documents, find the relevant policy, and provide that information to the AI.
The process can look like this:
Employee Question → Search Company Documents → Find Relevant Policy → Generate Answer
This can help employees find information without manually searching through multiple internal documents.
Customer Support
A customer support AI can use RAG to answer questions using a company's current product information and support documents.
For example, a customer might ask:
“How can I return this product?”
The system can search the company's return policy and support documentation, retrieve the relevant information, and use it to generate a response.
The process can look like this:
Customer Question → Search Support Information → Retrieve Relevant Policy → Generate Response
This allows the AI to provide answers based on the company's support information rather than relying only on general knowledge.
PDF and Document Q&A
RAG can also be used to let users ask questions about large documents.
For example, a user could upload a long report and ask:
“What does this report say about the company's AI strategy?”
The system can search the document, find the relevant sections, and provide them to the AI as context.
The process can look like this:
Question → Search Document → Retrieve Relevant Sections → Generate Answer
This can make it easier to find specific information without reading an entire document manually.
Product Documentation
A software company can connect an AI assistant to its product documentation.
For example, a developer might ask:
“How do I configure this feature?”
The RAG system can search the documentation for the relevant instructions and provide the appropriate section to the AI.
The process can look like this:
Developer Question → Search Documentation → Find Relevant Instructions → Generate Answer
If the documentation is updated, the connected information can also be updated so that future searches can use the newer content.
Research and Knowledge Retrieval
RAG can also help researchers work with large collections of specialized documents, such as research papers, reports, or patents.
For example, a researcher could ask a question about a specific topic. The system can search the available collection and retrieve the documents or sections that are most relevant to the question.
The process can look like this:
Research Question → Search Knowledge Collection → Retrieve Relevant Information → Generate Answer
This can help researchers find relevant information across a large collection of documents more efficiently.
What Do These Examples Have in Common?
These examples show that RAG can be used as part of a larger AI application to connect an AI model with information from a specific source.
The key idea is simple: RAG helps bring relevant information into the AI's context when it is needed.
What Are the Limitations of RAG?
RAG can help an AI system use external information, but it does not automatically make every answer correct.
A RAG system depends on several steps, from preparing documents and searching for relevant information to providing that information to the AI model. Problems at any of these steps can affect the final answer.
RAG May Fail to Find the Right Information
Sometimes, the information needed to answer a question exists in the knowledge base, but the retrieval system fails to find it.
If no relevant information is retrieved, the AI has little or no useful context to work with.
This can happen because of problems with the search method, document structure, chunking, or the way the user's question is processed.
RAG May Retrieve the Wrong Information
A system can also retrieve information that looks relevant but does not actually answer the user's question.
For example, a company may have several documents containing similar terms but different policies. If the wrong document is retrieved, the AI may use that information when generating its answer.
This is an important RAG limitation because the model can only work with the context it receives.
RAG May Retrieve Outdated Information
RAG does not automatically know which document is the newest.
For example, a company might have an old 2023 policy and an updated 2026 version. If both documents are stored in the knowledge base, the retrieval system may return the older version if it is considered relevant to the question.
This is why RAG systems may need metadata such as dates, document versions, or update status to help prioritize current information.
RAG May Retrieve Too Little Information
Finding a relevant document is not always enough.
If a document has been divided into very small chunks, the retrieved section may not contain all the information needed to answer a question.
For example, an important condition might appear in the paragraph immediately before or after the retrieved chunk. Without that additional context, the AI may produce an incomplete answer.
This is one reason how documents are divided and how much context is retrieved matters in RAG.
RAG May Retrieve Too Much Information
The opposite problem can also occur.
If a system retrieves too many chunks or includes large amounts of unnecessary text, the AI model may have difficulty focusing on the most relevant information.
More information is not always better.
A well-designed RAG system needs to find enough relevant information without overwhelming the model with unnecessary content.
RAG Cannot Fix Incorrect Source Documents
RAG can retrieve information from a source, but it does not automatically verify whether that source is correct.
If a company document contains an incorrect number, outdated instruction, or contradictory information, RAG may retrieve that information and provide it to the AI.
This means the quality of the source material is also important.
Good retrieval cannot compensate for unreliable source information.
RAG Creates Privacy and Security Challenges
RAG systems may connect AI models to private documents and databases.
This creates an important security concern: the system must make sure users can access only the information they are authorized to see.
For example, an employee should not be able to retrieve confidential information simply by asking the AI a cleverly worded question.
Organizations therefore need appropriate access controls, permissions, and security measures when connecting private data to a RAG system.
Why Retrieval Quality Matters
These limitations show why retrieval quality is one of the most important parts of RAG.
The AI model can generate a well-written answer, but if the system retrieves missing, outdated, irrelevant, or incomplete information, the final answer may also be unreliable.
This leads to a simple principle:
Better retrieval → Better context → Better chance of a useful answer
However, RAG should not be viewed as a way to eliminate hallucinations completely.
RAG helps an AI system use relevant external information, but it does not guarantee that every answer will be accurate.
That is why important information should still be checked against the original sources, especially when the answer could affect an important decision.
RAG vs. Search Engines
RAG and search engines both retrieve information, but they use that information in different ways.
A search engine finds relevant information and shows the results to the user.
For example, when you search for “company remote work policy,” a search engine may return a list of relevant web pages or documents for you to read.
RAG takes a different approach.
A RAG system retrieves relevant information and provides it to an AI model as context. The AI model then uses that information to generate an answer.
The basic difference is:
Search Engine: Find information → Show results to the user
RAG: Find information → Provide context to the AI → Generate an answer
Can RAG Use a Search Engine?
Yes. A RAG system can use a search engine as a source for retrieving information, depending on how the system is built.
For example, a RAG application could use a search engine to find relevant web pages, while another RAG system might search a company's document database, vector database, or other connected information source.
After the search, the relevant information is retrieved and provided to the AI model as context.
So, a search engine is primarily designed to help users find information, while RAG uses retrieval as part of a larger system that provides external information to an AI model before it generates an answer.
The two can therefore work together, but a search engine and RAG are not the same thing.
RAG vs. AI Agents
RAG and AI Agents are not competing technologies. An AI Agent can use RAG as a tool to retrieve information when it needs it.
For example, an AI Agent working on a task may need information from a company's internal documents. It can use a RAG system to search those documents, retrieve the relevant information, and then use that information as context for the next step of its task.
The relationship can be summarized simply:
RAG → Retrieves relevant information
AI Agent → Decides when and how to use tools to complete a task
This means RAG can become one of the information-retrieval tools available to an AI Agent.
For example, an Agent might use RAG to find a company's internal policy, use another tool to check a database, and then use the retrieved information to complete the task.
So, RAG provides access to relevant knowledge, while an AI Agent can decide when that knowledge is needed and what to do with it.
Simple Summary
RAG, or Retrieval-Augmented Generation, connects an AI model to external information. When a user asks a question, the system retrieves relevant information and provides it to the model as context before generating an answer.
RAG is useful for working with private, specialized, or frequently updated information. However, the quality of the answer still depends on the information retrieved and the quality of the source.
Key Takeaways
- RAG retrieves external information and provides it to an AI model as context.
- It is useful for information that is private, specialized, or frequently updated.
- Retrieval quality matters because missing, irrelevant, outdated, or incorrect information can affect the final answer.
- RAG can make AI answers more traceable by connecting them to source information and metadata.
- RAG and Fine-Tuning serve different purposes: RAG provides information, while Fine-Tuning can change a model's behavior or output patterns.
- RAG does not guarantee accurate answers or eliminate errors.
Question for Readers
If you could connect an AI assistant to one source of information, what would you choose?
Would you connect it to your company's documents, your personal files, product documentation, or something else?
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RAG is one way to connect AI models to information outside the model itself. In the next post, we will explore another important AI concept and continue building a simple understanding of how modern AI systems work and how they are used in the real world.
Continue Learning
If you are new to the concepts behind RAG, these guides can help you build the foundation first:
FAQ
Q. What does RAG stand for?
A. RAG stands for Retrieval-Augmented Generation. It is a method that allows an AI model to retrieve relevant information from external sources and use that information when generating an answer.
Q. Why is RAG useful?
A. RAG is useful when an AI needs access to specific, private, or frequently changing information that may not be available from the model alone.
Q. Does RAG retrain an AI model?
A. No. RAG normally provides relevant external information to the model as context instead of retraining the underlying model.
Q. Is RAG the same as a vector database?
A. No. A vector database is a system that can store and search embeddings. RAG is a broader approach that uses retrieval as part of the process of generating an answer.
Q. Is RAG better than Fine-Tuning?
A. Neither is always better. RAG is generally useful for giving an AI access to external knowledge, while Fine-Tuning can be useful for changing how a model behaves or produces its output. In some applications, they can also be used together.
Q. Can RAG prevent AI hallucinations?
A. No. RAG can provide relevant source information that may help reduce unsupported answers, but it cannot guarantee that an AI will never produce incorrect information.
Q. Can an AI Agent use RAG?
A. Yes. An AI Agent can use a RAG system as a tool for retrieving information when it needs knowledge from a specific document collection or database.
Q. What is the main limitation of RAG?
A. One of the biggest limitations is retrieval quality. If RAG retrieves missing, irrelevant, outdated, or incorrect information, the final answer may also be unreliable.