Definitions, differences, simple examples, basic workflow, and common mistakes.

A clear 90-second answer

Say this first: Retrieval-augmented generation fetches relevant, approved context at answer time so a model can ground its response in current source material.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply RAG architecture, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

request or change → guardrail / validation → RAG architecture → observable result → owner review

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: document ingestion is a choice between approaches with different strengths. The useful answer is the decision rule, not a dictionary definition.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply document ingestion, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Choose the option that fits the workload and constraints; do not present one option as universally superior.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: Chunking splits source material into retrieval units. The boundary and size affect whether a retrieved passage contains enough context to support an answer.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply chunking strategy, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

request or change → guardrail / validation → chunking strategy → observable result → owner review

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: “embedding model choice is the part of the system that helps us deliver the intended outcome safely and predictably. I would explain it using a small customer scenario, then show the check that proves it worked.”

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply embedding model choice, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: The important point about vector search is how an engineer recognizes the unsafe path early and prevents it from becoming customer impact.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply vector search, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Start with containment and evidence. Changing several variables at once makes the incident harder to understand.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: hybrid search should be explained through its purpose, the boundary where it applies, and the evidence that shows it is working.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply hybrid search, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: metadata filtering is a choice between approaches with different strengths. The useful answer is the decision rule, not a dictionary definition.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply metadata filtering, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Choose the option that fits the workload and constraints; do not present one option as universally superior.
• Call out prompt injection and unsupported answers and the control that reduces it.

Concrete check

SELECT COUNT(*) AS rows, MAX(loaded_at) AS freshest FROM <table>;

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: reranking should be explained through its purpose, the boundary where it applies, and the evidence that shows it is working.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply reranking, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: “query rewriting is the part of the system that helps us deliver the intended outcome safely and predictably. I would explain it using a small customer scenario, then show the check that proves it worked.”

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply query rewriting, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: The important point about context assembly is how an engineer recognizes the unsafe path early and prevents it from becoming customer impact.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply context assembly, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Start with containment and evidence. Changing several variables at once makes the incident harder to understand.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: citation grounding should be explained through its purpose, the boundary where it applies, and the evidence that shows it is working.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply citation grounding, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: answer faithfulness is a choice between approaches with different strengths. The useful answer is the decision rule, not a dictionary definition.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply answer faithfulness, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Choose the option that fits the workload and constraints; do not present one option as universally superior.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: hallucination reduction should be explained through its purpose, the boundary where it applies, and the evidence that shows it is working.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply hallucination reduction, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: “RAG evaluation metrics is the part of the system that helps us deliver the intended outcome safely and predictably. I would explain it using a small customer scenario, then show the check that proves it worked.”

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply RAG evaluation metrics, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: The important point about golden dataset creation is how an engineer recognizes the unsafe path early and prevents it from becoming customer impact.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply golden dataset creation, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Start with containment and evidence. Changing several variables at once makes the incident harder to understand.
• Call out prompt injection and unsupported answers and the control that reduces it.

Concrete check

SELECT COUNT(*) AS rows, MAX(loaded_at) AS freshest FROM <table>;

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: Retrieval-augmented generation fetches relevant, approved context at answer time so a model can ground its response in current source material.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply prompt injection in RAG, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: access control in retrieval is a choice between approaches with different strengths. The useful answer is the decision rule, not a dictionary definition.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply access control in retrieval, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Choose the option that fits the workload and constraints; do not present one option as universally superior.
• Call out prompt injection and unsupported answers and the control that reduces it.

Concrete check

Review the least-privilege policy, then test the denied path as well as the allowed path.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: index refresh strategy should be explained through its purpose, the boundary where it applies, and the evidence that shows it is working.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply index refresh strategy, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

request or change → guardrail / validation → index refresh strategy → observable result → owner review

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: “multi-document QA is the part of the system that helps us deliver the intended outcome safely and predictably. I would explain it using a small customer scenario, then show the check that proves it worked.”

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply multi-document QA, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: long-context vs RAG is a choice between approaches with different strengths. The useful answer is the decision rule, not a dictionary definition.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply long-context vs RAG, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Choose the option that fits the workload and constraints; do not present one option as universally superior.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: Retrieval-augmented generation fetches relevant, approved context at answer time so a model can ground its response in current source material.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply agentic RAG, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: Graph RAG basics is a choice between approaches with different strengths. The useful answer is the decision rule, not a dictionary definition.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply Graph RAG basics, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Choose the option that fits the workload and constraints; do not present one option as universally superior.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: latency optimization should be explained through its purpose, the boundary where it applies, and the evidence that shows it is working.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply latency optimization, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: “token cost reduction is the part of the system that helps us deliver the intended outcome safely and predictably. I would explain it using a small customer scenario, then show the check that proves it worked.”

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply token cost reduction, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• State the constraint that could change your decision, such as scale, data sensitivity, recovery target, or team ownership.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.

A clear 90-second answer

Say this first: The important point about observability for RAG is how an engineer recognizes the unsafe path early and prevents it from becoming customer impact.

Use a real scenario

Imagine an internal support assistant that answers from approved policy documents. The team must decide how to apply observability for RAG, verify the result, and explain the user impact. For a RAG Engineer, attach the explanation to an evaluation set and retrieval trace.

Show judgment

• name the concept, give one concrete use, and say how you would check the result.
• Start with containment and evidence. Changing several variables at once makes the incident harder to understand.
• Call out prompt injection and unsupported answers and the control that reduces it.

Evidence to mention

Track grounded-answer rate and p95 response time. Say what baseline you compared against, what would trigger a rollback or escalation, and who owns the follow-up.

Practice prompt: Keep the scope small. Do not claim production ownership you have not had.