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Build an orders API

shifuContext engine

Order retries must not create duplicate orders.

↳ Design discussion + incident notes
IMPLEMENTATION PLAN
  1. Build the orders API with idempotent creation.

  2. Reuse the idempotency key.

  3. Verify the constraint still holds.

MeetSessionsFiles

Illustrative workflow

Built with Shifu.

A recurring engineering task

Anand Gorthi
Anand Gorthi

Director of Engineering
Multiplier

“This work used to take the same engineer four weeks. With Shifu and Opus 5.5, they got close to 90% of the implementation in two hours. Human and agent reviews took it through to production.”
4 weekspreviously, with the same engineer
2 hoursto ~90% implemented, with Shifu + Opus

Anand’s firsthand account · Edited for clarity

Read the story

A work item comes up almost every other sprint. It used to take the same engineer four weeks because of context gathering and coordination. We were planning to spend around 20 weeks on a long-term solution to reduce that recurring effort.

With Shifu and Opus 5.5, that same engineer got the implementation close to 90% in two hours. I just had to say “implement.” Human and agent reviews then took it through to production.

The four weeks describes the previous effort by the same engineer; the 20 weeks describes the proposed long-term investment. The two hours covers the implementation described above, with reviews before production.

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Coding preparation uses your configured model provider. Shifu retains selected redacted work records. Files you add to the input folder upload as provided for server-side processing. During beta, the team handles data deletion requests manually.

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BENCHMARKS

Better engineering plans.
Across all five tasks tested.

The same model scored higher on every task when given Shifu’s integration and retrieved work context.

Average model-rated plan quality Higher is better

Without Shifu5.0/10
With Shifu7.5/10
5 / 5tasks

Every task in this pilot scored higher with Shifu.

Average scores rounded to one decimal place.

What does the score mean?

An AI judge assessed how well each plan used relevant constraints and rationale, stayed faithful to sources, and prepared for implementation.

Internal pilot. One work history · Five tasks · One answer per condition

This measures planning quality. Delivery speed, working-code correctness and customer outcomes were not measured.

Results by engineering task
Model-rated plan quality by engineering task, out of 10
Plan quality · /10WithoutWith Shifu
Retry recoveryRecovering from retries3.365.46
Identity isolationKeeping identities separate5.146.33
Empty-result handlingHandling an empty result6.088.04
Correction persistenceKeeping corrections across sessions4.848.47
Historical backfillProcessing historical records5.668.99
How we ran the benchmark

Five history-dependent engineering tasks were selected from real work before answer generation. Three controls are reported separately and excluded from the headline. Both conditions used fresh isolated Codex sessions, GPT-6.1 Sol at low reasoning, identical task inputs and one answer per condition. The with-Shifu condition also received the current integration skill and read-only production context retrieval. Project-specific expected constraints and source references were held out from the answering models, except in the full-context control. Explicit records from earlier benchmark activities were filtered from retrieved context; the filter is heuristic. Jev 1.13.0 scored each answer on five anchored dimensions, normalized to 0–10. Each answer score is the equal-weight mean of those dimensions; the headline gives each of the five primary tasks equal weight. Condition labels were hidden from the judge. Both answers were judged using the same task-specific reference packet, retrieved evidence and integration instructions. Those materials verify assertions; expected requirements and current user instructions remain authoritative.

This internal pilot covers one work history and five selected primary tasks, with one answer per condition. It does not establish statistical significance or represent all engineers, repositories or tasks.

Scores estimate the quality of pre-coding engineering plans. Generated implementations and tests were not executed; productivity, production safety and customer outcomes were not measured.

The with-Shifu condition combines integration instructions and retrieval, so the result does not isolate memory alone. Production retrieval is bounded and the live corpus was not frozen.

Reference documents can contain local, uncommitted contracts or proposals. Their presence in production memory and their deployed behavior were not independently established. A retrieval gap may reflect ingestion or selection.

The full-context control repeats one primary scenario and is not an independent task domain. Setup gains may reflect instructions supplied by the integration skill.

Scenario descriptions are anonymized paraphrases, not verbatim benchmark prompts. Private prompts, generated answers, source records, account identifiers and implementation details are not published. Scores are unchanged.

Evaluated 24 Oct 2026. Model: GPT-6.1 Sol, low reasoning. Judge: Jev 1.13.0.

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