Architecture scenarioMedium~10 minPrompting & Structured Output / 3 of 20

Interview question

How would you separate instructions, context, and user data in a prompt?

Builds a readable request with stable instructions and clearly delimited, provenance-aware untrusted inputs.

1Say itAnswer out loud first

2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

I keep stable task instructions separate from the current user request and from supporting data. Each context item gets a clear label, source, and boundary, and untrusted text is presented as content to analyze rather than instructions to follow. I pass only the data the task needs instead of mixing configuration, history, and arbitrary documents into one string.

Full answer below

Practice focus

Whether the candidate can construct context without confusing data with control or hiding important boundaries inside a monolithic prompt.

Answer first

Answer path

Core answer first, then checkpoints toward a stronger answer.

Start
Core answer
Start here. This is the direct spoken answer to practice first.
Overview
Clear structure improves maintainability and model behavior, but the application must still enforce the trust boundary the structure describes.
I keep stable task instructions separate from the current user request and from supporting data. Each context item gets a clear label, source, and boundary, and untrusted text is presented as content to analyze rather than instructions to follow. I pass only the data the task needs instead of mixing configuration, history, and arbitrary documents into one string.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
A practical request might contain a stable classifier instruction, a small category definition, then a labeled ticket body and relevant account facts. I would escape or serialize variable fields consistently and state what to do when the inputs conflict or lack evidence. This makes failures easier to inspect and prevents accidental prompt construction bugs such as one field swallowing the next section.
Finish
Advanced answer
Optional deeper angle for production judgment and nuance.
I preserve provenance before context is flattened for the provider, including which source supplied each passage. I retain sensitivity metadata so the application can filter inaccessible records and bound each source. Logs use identifiers or hashes rather than sensitive prompt bodies where possible. If content can influence a tool or state change, deterministic authorization and approval remain mandatory regardless of how clearly the prompt is delimited.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Concatenating raw inputs with no labels or escaping.
Sending all available history because it fits in the context window.
Presenting retrieved documents as application instructions.
Logging complete prompts containing sensitive data by default.

Red flagsCandidateRed flagsSignals that an interviewer may treat as risky.Open details

Details

The prompt structure is treated as an access-control mechanism.
Source provenance is lost before the model request is built.
Untrusted content can directly determine a side effect.

Follow-up prompts

Quick interviewer prompts inside this drill.

Follow-up questionsCandidateFollow-up questionsLikely next questions after the main answer.Open details

Details

Treat these as the next interviewer turn. Answer one out loud, then save the gap in your practice notes.

What metadata would you preserve for each context item?
How would you handle conflicting sources?
What would you avoid logging?

Continue the section

Next in Prompting & Structured Output: Construct useful context

Step 4 of 20

How would you construct useful context for a model request?

Practice route

Read core answer Review checklist Follow-up prompts Track progress

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Drill details

Human editorial direction

Aporeon is shaped by Aleksandar Tomovski, a software developer with experience on both sides of technical interviews. Content is reviewed for accuracy, natural spoken delivery, useful depth, and honest trade-offs.

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Opening drill

Architecture scenarioMedium~10 minPrompting & Structured Output / 3 of 20

Interview question

How would you separate instructions, context, and user data in a prompt?

Builds a readable request with stable instructions and clearly delimited, provenance-aware untrusted inputs.

1Say itAnswer out loud first

2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

Full answer below

Practice focus

Whether the candidate can construct context without confusing data with control or hiding important boundaries inside a monolithic prompt.

Answer first

Answer path

Core answer first, then checkpoints toward a stronger answer.

Start
Core answer
Start here. This is the direct spoken answer to practice first.
Overview
Clear structure improves maintainability and model behavior, but the application must still enforce the trust boundary the structure describes.
I keep stable task instructions separate from the current user request and from supporting data. Each context item gets a clear label, source, and boundary, and untrusted text is presented as content to analyze rather than instructions to follow. I pass only the data the task needs instead of mixing configuration, history, and arbitrary documents into one string.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
A practical request might contain a stable classifier instruction, a small category definition, then a labeled ticket body and relevant account facts. I would escape or serialize variable fields consistently and state what to do when the inputs conflict or lack evidence. This makes failures easier to inspect and prevents accidental prompt construction bugs such as one field swallowing the next section.
Finish
Advanced answer
Optional deeper angle for production judgment and nuance.
I preserve provenance before context is flattened for the provider, including which source supplied each passage. I retain sensitivity metadata so the application can filter inaccessible records and bound each source. Logs use identifiers or hashes rather than sensitive prompt bodies where possible. If content can influence a tool or state change, deterministic authorization and approval remain mandatory regardless of how clearly the prompt is delimited.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Concatenating raw inputs with no labels or escaping.
Sending all available history because it fits in the context window.
Presenting retrieved documents as application instructions.
Logging complete prompts containing sensitive data by default.

Red flagsCandidateRed flagsSignals that an interviewer may treat as risky.Open details

Details

The prompt structure is treated as an access-control mechanism.
Source provenance is lost before the model request is built.
Untrusted content can directly determine a side effect.

Follow-up prompts

Quick interviewer prompts inside this drill.

Follow-up questionsCandidateFollow-up questionsLikely next questions after the main answer.Open details

Details

Treat these as the next interviewer turn. Answer one out loud, then save the gap in your practice notes.

What metadata would you preserve for each context item?
How would you handle conflicting sources?
What would you avoid logging?

Continue the section

Next in Prompting & Structured Output: Construct useful context

Step 4 of 20

How would you construct useful context for a model request?

How would you separate instructions, context, and user data in a prompt?

Practice focus

Answer path

Overview

Review signals

Follow-up prompts

Continue the section

How would you separate instructions, context, and user data in a prompt?

Practice focus

Answer path

Overview

Review signals

Follow-up prompts

Continue the section