RAG chatbot
Words made simple
A few useful meanings before you read the explanation.
- RAG
- Find relevant information first, then give it to a model to help answer the question. The answer is only as reliable as the information and checks used.
- API
- A way for one piece of software to ask another for data or an action. A claims API might return the current status of a claim.
- Retrieval
- Finding and fetching information that is useful for the current question.
More words from this lesson
- Grounding
- Connecting an answer to supplied evidence, such as a policy document or a verified database result.
- Authentication
- Checking who a user or service is, such as by verifying a login session.
- Authorization
- Checking what that identity is allowed to read or do. Being logged in does not grant access to every record.
- Evaluation
- Checking results against examples and clear criteria to find whether the system works well and where it fails.
Overview
A RAG chatbot answers conversations using retrieved knowledge. The dialogue interface and retrieval pipeline are separate responsibilities.
Why this matters
Private or changing information cannot be safely answered from model knowledge alone.
An everyday example
A support employee consults the correct handbook while talking.
A technical example
/chat retrieves eligible policy passages and returns answer plus source IDs.
How it works
- Authenticate
- Resolve follow-up
- Retrieve
- Build context
- Generate
- Validate citations.
Architecture and flow
Chat API → retrieval → grounded model → cited answer.
The insurance assistant example
Explain reporting instructions from the customer's active policy.
When to use it
Document-grounded conversation.
When to avoid it
Account status belongs to an exact authorized service lookup.
Interview answer
“I combine conversation handling with eligible evidence retrieval and separate their evaluation.”
Benefits, trade-offs, and production considerations
Manage ingestion freshness, source versions, abstention, and follow-up rewriting; failures should identify whether search or generation failed.
Can you explain it without the notes?
RAG chatbot
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
A RAG chatbot answers conversations using retrieved knowledge. The dialogue interface and retrieval pipeline are separate responsibilities.
Turn this concept into a clear answer.
Practice this conceptAdapted from 13_AI.MD · “RAG chatbot”. Examples are study fragments; framework behavior can depend on your version.