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AI Architect, LLM Systems

What happens to your Dril.

Mock stays private to you. Live is what the enterprise actually sees once you’ve taken a Job Dril, self-served from the Dril Board or by invite.

Interview Dril · Mock · Private to you

Overstated claimsUnder-stretched
We never found your limit on this one, so this score is a floor. You may be stronger than it shows.

The exchange that broke

  1. QInterviewer asked, 09:20: You mentioned leading the vector search migration. Walk me through a decision that was yours alone.09:20
  2. AYou answered, 09:45: “I led that migration, mostly.”09:45
  3. ›Interviewer probed, 09:58: What part wasn't yours?09:58
  4. AYou answered, 10:15: “The indexing strategy was actually decided by another engineer on the team. I mostly handled the cutover.”10:15
  5. ›Interviewer probed, 10:28: If the cutover had failed mid-migration, what was your rollback plan?10:28
  6. AYou answered, 10:41: “We were fairly confident it would work.”10:41

Ownership 2 / 4

Claims 'I led' but the indexing decision stayed someone else's.

What held up under probing

LLM systems, 5+ yearsVerified

War story held under P1 and P2 with specific, lived detail.

02:25You“I built out our multi-agent LLM orchestration layer. It handles routing between four different model backends depending on task complexity.”
Production RAG pipeline ownershipVerified

Named a real tradeoff and the failure that prompted it.

07:20You“The RAG eval pipeline flagged silent regressions before they ever hit production. That's the whole reason I built it that way.”
Inference cost optimizationOverstated

Demonstrated depth: quantized one model family, not the full inference stack

Real work on one model family; the claimed stack-wide 60% cost cut was not demonstrated.

11:10You“Honestly the quantization only covered our largest model family. I didn't get to the rest before I left.”
Vector search migration experienceUnsupported

Could not describe a real decision they owned in this dril.

09:45You“I led that migration, mostly.”

How the answers scored

76/ 100Good

Rests on 5 of 5 dimensions

Five weighted dimensions, each with its own evidence.

RobustnessDoes it survive the probe?
3 / 4

Survived P2, added specific detail on the fallback chain.

03:10You“We have a fallback chain. If the primary model times out it retries against a secondary, then a cached response if both fail.”
SubstanceDo they know the craft?
4 / 4

Correct with lived detail; named the regression the eval framework caught.

07:20You“The RAG eval pipeline flagged silent regressions before they ever hit production.”
OwnershipDid they actually do it?
2 / 4

Claims 'I led' but the indexing decision stayed someone else's.

09:45You“I led that migration, mostly.”
RecoveryWhat happens at the edge of their knowledge?
3 / 4

Named the real scope gap and reasoned from first principles instead of bluffing.

11:10You“Honestly the quantization only covered our largest model family. I didn't get to the rest before I left.”
ClarityCan a listener follow it first time?
3 / 4

Answer-first, followable on the first pass.

02:25You“I built out our multi-agent LLM orchestration layer. It handles routing between four different model backends.”

What to fix

  • Ownership: Claims 'I led' but the indexing decision stayed someone else's.

Private to you. Never shown to any employer.

  • hedging · 09:45

    Leaned on “mostly” instead of naming exactly what you owned.

    Lead with the specific decision you made, then explain who else was involved.

  • filler

    Frequent softeners: “fairly,” “honestly.”

    Swap a qualifier for a real number whenever you have one.

Interview video & transcript

Interview video (example)

On a real Job Dril Card, this shows the candidate's full interview video.

  1. QTell me about a production system you've built end to end.02:10
  2. A“I built out our multi-agent LLM orchestration layer. It handles routing between four different model backends depending on task complexity.”02:25
  3. ›Walk me through what happens when one of those backends goes down mid-request.02:50
  4. A“We have a fallback chain. If the primary model times out it retries against a secondary, then a cached response if both fail.”03:10
  5. QTell me about the RAG evaluation work.06:50
  6. A“The RAG eval pipeline flagged silent regressions before they ever hit production. That's the whole reason I built it that way.”07:20
  7. QYou mentioned leading the vector search migration. Walk me through a decision that was yours alone.09:20
  8. A“I led that migration, mostly.”09:45
  9. ›What part wasn't yours?09:58
  10. A“The indexing strategy was actually decided by another engineer on the team. I mostly handled the cutover.”10:15
  11. ›If the cutover had failed mid-migration, what was your rollback plan?10:28
  12. A“We were fairly confident it would work.”10:41
  13. QYou said you cut inference cost 60% via quantization. Across how much of the stack?10:50
  14. A“Honestly the quantization only covered our largest model family. I didn't get to the rest before I left.”11:10

Job Dril · Live · Shared with the enterprise

This is exactly what the enterprise reviews before shortlisting you.

You

AI Architect, LLM Systems

Verification checklist

Every mark earned live, in the exchange itself.

LLM systems, 5+ yearsVerified

War story held under P1 and P2 with specific, lived detail.

02:25Candidate“I built out our multi-agent LLM orchestration layer. It handles routing between four different model backends depending on task complexity.”
Production RAG pipeline ownershipVerified

Named a real tradeoff and the failure that prompted it.

07:20Candidate“The RAG eval pipeline flagged silent regressions before they ever hit production. That's the whole reason I built it that way.”
Inference cost optimizationOverstated

Demonstrated depth: quantized one model family, not the full inference stack

Real work on one model family; the claimed stack-wide 60% cost cut was not demonstrated.

11:10Candidate“Honestly the quantization only covered our largest model family. I didn't get to the rest before I left.”
Vector search migration experienceUnsupported

Could not describe a real decision they owned in this dril.

09:45Candidate“I led that migration, mostly.”

Requirement match

Measured against what the candidate demonstrated in the room.

Must-have

LLM systems, 5+ yearsMust-have
Exceeds
Production RAG pipeline ownershipMust-have
Met

Preferred

Inference cost optimizationPreferred
Not met
Vector search migration experiencePreferred
Not met

Evaluation

76/ 100Good

Rests on 5 of 5 dimensions

Five dimensions, not one pass or fail number.

RobustnessDoes it survive the probe?
3 / 4

Survived P2, added specific detail on the fallback chain.

03:10Candidate“We have a fallback chain. If the primary model times out it retries against a secondary, then a cached response if both fail.”
SubstanceDo they know the craft?
4 / 4

Correct with lived detail; named the regression the eval framework caught.

07:20Candidate“The RAG eval pipeline flagged silent regressions before they ever hit production.”
OwnershipDid they actually do it?
2 / 4

Claims 'I led' but the indexing decision stayed someone else's.

09:45Candidate“I led that migration, mostly.”
RecoveryWhat happens at the edge of their knowledge?
3 / 4

Named the real scope gap and reasoned from first principles instead of bluffing.

11:10Candidate“Honestly the quantization only covered our largest model family. I didn't get to the rest before I left.”
ClarityCan a listener follow it first time?
3 / 4

Answer-first, followable on the first pass.

02:25Candidate“I built out our multi-agent LLM orchestration layer. It handles routing between four different model backends.”

What stood out

Strongest

“The RAG eval pipeline flagged silent regressions before they ever hit production. That's the whole reason I built it that way.”

07:20

Weakest

“I led that migration, mostly.”

09:45

Most revealing

“Honestly the quantization only covered our largest model family. I didn't get to the rest before I left.”

11:10

Named the real scope plainly instead of letting the 60% figure stand unqualified.

Flags

Overstated claimsUnder-stretched

Interview video & transcript

Interview video (example)

On a real Job Dril Card, this shows the candidate's full interview video.

  1. QTell me about a production system you've built end to end.02:10
  2. A“I built out our multi-agent LLM orchestration layer. It handles routing between four different model backends depending on task complexity.”02:25
  3. ›Walk me through what happens when one of those backends goes down mid-request.02:50
  4. A“We have a fallback chain. If the primary model times out it retries against a secondary, then a cached response if both fail.”03:10
  5. QTell me about the RAG evaluation work.06:50
  6. A“The RAG eval pipeline flagged silent regressions before they ever hit production. That's the whole reason I built it that way.”07:20
  7. QYou mentioned leading the vector search migration. Walk me through a decision that was yours alone.09:20
  8. A“I led that migration, mostly.”09:45
  9. ›What part wasn't yours?09:58
  10. A“The indexing strategy was actually decided by another engineer on the team. I mostly handled the cutover.”10:15
  11. ›If the cutover had failed mid-migration, what was your rollback plan?10:28
  12. A“We were fairly confident it would work.”10:41
  13. QYou said you cut inference cost 60% via quantization. Across how much of the stack?10:50
  14. A“Honestly the quantization only covered our largest model family. I didn't get to the rest before I left.”11:10
Signal:StrongYou decide, always.

Scored only on what was answered. Checked against the transcript.

Built to be forwarded internally without editing.

This is the talent’s own view of one example Dril. See the same artifact from the enterprise side.