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How TechInView Evaluates You
TechInView (TIV) — AI DSA mock interviews with Tia, your AI interviewer.
When you finish a session on TechInView, our scoring model reviews what you said to Tia and the code you wrote in the interview editor. Each dimension is scored from 0–100. Your overall score is the weighted sum of those five scores. That overall maps to a hire-style recommendation so you can benchmark yourself against a strong loop.
The five dimensions
TechInView's rubric weights mirror how many FAANG-style panels emphasize problem solving and code, while still rewarding communication, depth, and testing in an AI interview.
Problem Solving
30% weightClarification, approach, edge cases
TechInView evaluates whether you clarify requirements and constraints, propose a reasonable approach, and reason about edge cases (empty input, duplicates, bounds) before leaning on code. Strong scores usually mean you can defend why your approach fits the problem and adjust when Tia pushes back like a real interviewer.
Code Quality
25% weightReadability, naming, idioms
TechInView scores how readable and maintainable your solution is in the session editor: naming, structure, control flow, and whether you avoid needless complexity. Idiomatic use of your language and small refactors when you notice smell help—panels care that someone else could review or extend your code.
Communication
20% weightThinking aloud, structured explanation
In TechInView AI interviews, Tia hears how you explain your plan, narrate trade-offs, and respond to hints. You do not need a polished speech—structured, honest explanation (including when you are stuck) scores better than long silence.
Technical Knowledge
15% weightComplexity analysis, trade-offs
TechInView weights depth in your answers: correct time and space complexity, why your data structures fit the constraints, and how you compare alternatives (e.g. extra space vs. in-place). Calibrated follow-ups and crisp answers when discussing bottlenecks or optimizations matter.
Testing
10% weightEdge cases, proactive testing
TechInView credits walking through examples, calling out edge cases, and checking your logic when something fails—whether you run tests in the built-in editor or trace by hand. Proactively testing corner cases and fixing bugs when output is wrong looks stronger than only happy-path code.
Overall = (Problem Solving × 30%) + (Code Quality × 25%) + (Communication × 20%) + (Technical Knowledge × 15%) + (Testing × 10%), each using the 0–100 score for that dimension.
Hire recommendation bands
On TechInView, your hire recommendation comes from your weighted overall score—not from any single dimension in isolation.
- Strong HireOverall 85–100
- HireOverall 70–84
- Lean HireOverall 55–69
- Lean No HireOverall 40–54
- No HireOverall 0–39
Sample report
Below is a static preview of what TechInView shows after a Tia interview: summary, radar chart, and per-dimension cards. A real run also includes your transcript and code review on your TechInView results page.
Solid performance: clear approach, working solution, and reasonable complexity discussion. Communication was good with room to be more vocal during debugging. Overall aligned with a hire-level bar for this problem.
Performance Breakdown
Dimension breakdown
Problem Solving
30% weightYou asked good clarifying questions about duplicates and empty inputs before coding. The two-pointer approach was appropriate; consider stating the invariant you maintain across moves earlier in the discussion.
Code Quality
25% weightNaming was clear and the loop structure was easy to follow. Minor nit: extracting the swap into a small helper would match common style for readability in longer solutions.
Communication
20% weightYou explained your thinking at a steady pace. A few pauses were long; briefly narrating what you are stuck on helps Tia coach you faster.
Technical Knowledge
15% weightTime and space complexity were correct. You mentioned stability trade-offs when relevant; deepening one sentence on why the hash map beats sorting for this constraint would strengthen the answer.
Testing
10% weightYou walked the main example and one edge case. Adding a quick check for single-element or all-equal inputs would mirror what many interviewers expect before they say “looks good.”
Ready for your own TechInView report?