AI Skill Report Card

Engineering Reverse Analysis

A-82·Nov 7, 2026·Source: Web
14 / 15

Give the model an assembly (drawing, photo, description, or part) and this three-part prompt:

Analyze this assembly/unit:

1. DECOMPOSITION: Break it down into its constituent components/parts.
   List each component, its apparent function, and how it connects to neighbors.

2. CONSTRAINTS: For each component, identify likely materials and specs,
   citing only recognized standards (GOST, ISO, ASTM, DIN). If a material/spec
   cannot be tied to a standard, say so explicitly — do not guess.

3. VERIFICATION: For every claim above, mark it as:
   - [VERIFIED] — directly observable from provided data
   - [INFERRED] — reasonable engineering assumption, state the reasoning
   - [UNCERTAIN] — guess/pattern-match, requires engineer confirmation

Do not calculate tolerances, strength, or load-bearing values. Flag these as
"requires CAE/manual calculation" instead.

Output feeds into a draft ТЗ (technical specification) — never directly into production or CAD without engineer sign-off.

Recommendation▾
Add an example showing a more complex multi-component assembly with nested hierarchy to demonstrate scalability
14 / 15

Progress:

  • Step 1: Define scope — single node/unit, not full system (keep decomposition tractable)
  • Step 2: Decompose — list components, hierarchy, connections
  • Step 3: Constrain — map each component to standards (GOST/ISO/ASTM); refuse to invent specs with no standard basis
  • Step 4: Verify — tag every statement VERIFIED / INFERRED / UNCERTAIN
  • Step 5: Route UNCERTAIN items back to the engineer as open questions, not as answers
  • Step 6: Package into draft ТЗ format; explicitly exclude final tolerances/strength calculations
  • Step 7: Engineer reviews, corrects, and performs final calculations in CAD/CAE

Never let AI output skip steps 4–7. The verification tagging is the trust mechanism — it's what lets a skeptical engineer accept the 80% that's solid and focus review time on the 20% flagged uncertain.

Recommendation▾
Include a template for the draft ТЗ output format itself, not just the analysis prompt
17 / 20

Example 1: Input: Photo of a bearing assembly from a competitor's gearbox. Output:

DECOMPOSITION:
- Outer race, inner race, rolling elements (balls), cage, two seals

CONSTRAINTS:
- Bearing type pattern matches deep-groove ball bearing per GOST 8338-75
- Seal material likely NBR rubber (common per GOST 9833) [INFERRED]
- Exact steel grade of races: cannot determine from photo alone [UNCERTAIN]

VERIFICATION:
- [VERIFIED] Component count and general geometry (visible in photo)
- [INFERRED] Bearing standard family, based on visual proportions
- [UNCERTAIN] Steel grade, tolerance class, load rating — requires
  physical inspection or manufacturer datasheet

NOT CALCULATED: load capacity, fatigue life — engineer to verify in CAE.

Example 2: Input: "Разбери этот кронштейн и предложи материал." Output: Decomposes into mounting plate + rib + bolt holes; proposes Steel 3 (Ст3) per GOST 380-2005 as [INFERRED] based on typical bracket applications; flags "exact grade depends on expected load, which was not provided" as [UNCERTAIN]; explicitly states no strength calculation performed.

Recommendation▾
Add a 'bad output' example showing what an unconstrained/overconfident AI response looks like for contrast
  • Start with reverse-engineering known/existing assemblies, not greenfield design — lower stakes, builds trust incrementally
  • Always anchor material/spec claims to a named standard; "best practice" or "commonly used" without a standard reference = reject
  • Keep the three tags (VERIFIED/INFERRED/UNCERTAIN) visually distinct and mandatory on every technical claim
  • Treat AI output as draft ТЗ or research acceleration only — never as final design input
  • Give engineers a one-line escape hatch to override: "If this looks wrong, it probably needs your judgment, not a better prompt"
  • Letting AI output tolerances, dimensions, or strength values as if final — always defer these to CAD/CAE and manual calc
  • Treating INFERRED claims as VERIFIED just because the AI sounds confident
  • Skipping the standards-constraint step — unconstrained material suggestions are a major source of engineer distrust
  • Introducing AI with a design task first instead of analysis/decomposition — this is what triggers sabotage/resistance
  • Omitting the "where might this be wrong" prompt — this self-flagging step is what makes engineers willing to adopt the tool
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Grade A-AI Skill Framework
Scorecard
Criteria Breakdown
Quick Start
14/15
Workflow
14/15
Examples
17/20
Completeness
18/20
Format
14/15
Conciseness
14/15