Design 1: RAG chatbot
Words made simple
A few useful meanings before you read the explanation.
- 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.
- LLM
- A model trained to work with language. It can write or summarize text, but its answer still needs checking against reliable information.
- 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.
- 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.
- Trace
- A linked record of the steps in one request, including model calls, retrieval and tools. It helps locate delays or failures.
- Chunking
- Dividing a long document into smaller pieces so relevant parts can be found and sent to the model.
Overview
A conversational interface over an eligible knowledge corpus. It combines a document pipeline with a request-time answer pipeline.
Why this matters
Users need understandable answers grounded in private sources.
An everyday example
A librarian talks through an applicable handbook.
A technical example
API returns {answer, sourceIds, answerable}.
How it works
- Ingest/version
- Authorize
- Retrieve/rerank
- Construct context
- Generate
- Validate.
Architecture and flow
UI → auth API → retriever/index → LLM → cited response.
The insurance assistant example
Explain active policy reporting instructions.
When to use it
Private/changing documents with clear source ownership.
When to avoid it
Exact live claim lookup needs a service tool.
Interview answer
“I design ingestion and answer-time retrieval separately, with ACLs, freshness, abstention, and independent evals.”
Benefits, trade-offs, and production considerations
- Use documents/chunks/jobs/messages records, queue ingestion, trace evidence IDs, and degrade to authorized search results if generation fails.
- Scale search and provider capacity independently.
Can you explain it without the notes?
Design 1: RAG chatbot
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
A conversational interface over an eligible knowledge corpus. It combines a document pipeline with a request-time answer pipeline.
Turn this concept into a clear answer.
Practice this conceptAdapted from 13_AI.MD · “Design 1: RAG chatbot”. Examples are study fragments; framework behavior can depend on your version.