Why people fail at interviews?

We all fail interviews at some point in our careers. What matters is whether we learn from them, put in the effort, and go through the grind to reach where we want to be.

That grind is the hard part. It requires discipline, consistency, and deliberate practice. Most people look for shortcuts or hacks. But interview success rarely comes from shortcuts. It comes from avoiding common mistakes that repeatedly lead to rejections.

This article covers some of the most common reasons why candidates fail interviews.

Not Clarifying Requirements

This is, by far, the most common reason for rejection. It becomes even more evident at senior levels.

Candidates hear the problem and immediately start coding.

• They assume constraints.
• They assume edge cases.
• They assume what the interviewer “probably meant.”

And they often end up solving the wrong problem.

The Reality

In many interviews, you are intentionally not given complete information. Ambiguity is part of the evaluation.

Your potential employer wants to see if you can:

• Detect missing constraints.
• Ask about edge cases (empty input, duplicates, large inputs, invalid values).
• Clarify expected behavior.
• Validate assumptions before implementation.

In real engineering, vague requirements are the norm. When working with stakeholders, you must proactively gather the necessary information to drive a project to success. If you blindly implement without clarifying, you introduce production risks.

Interviewers are testing whether you behave like a real engineer, not a coding machine trained to memorize patterns.

The Fix

Slow down. Do not jump the gun.

Before writing code:

• Restate the problem in your own words.
• Ask about edge cases.
• Ask about input size and constraints.
• Confirm the expected output format.
• Discuss trade-offs briefly.
• Outline a high-level plan before implementation.

Five minutes spent clarifying can prevent you from solving the wrong problem entirely.

Weak Fundamentals / Lack of Domain Knowledge

Most interview problems are not impossibly hard. They are usually a function of practice and conceptual clarity. However, solving them requires fluency in your domain.

There is a difference between knowing something and being able to implement it under pressure.

We’ve seen candidates who understand concepts in theory but freeze while implementing even straightforward solutions. While no one can know everything, you must have solid command over the core fundamentals of your field.

In the era of AI-assisted code generation, fundamentals matter even more.

You cannot effectively orchestrate AI agents, validate generated code, or debug complex systems unless your foundational knowledge is strong. We’ve seen candidates become so reliant on code generation tools that they struggle to write or debug basic code independently.

Do not outsource your thinking.

The Fix

• Take preparation seriously. Cover the fundamentals thoroughly.
• Use AI as a tool for deep dives, mock interviews, and clarification — not as a replacement for thinking.
• Practice a variety of curated questions. Try different difficulty levels, company-specific questions, or focus on core concepts.
• For machine coding rounds, go old school. Code completion is fine. Full code generation is not.

Lack of Communication

This is severely underestimated. An interviewer cannot evaluate your thinking if you don’t share it.

Many candidates:

• Go silent for long stretches.
• Write code without explaining their decisions.
• Speak only when asked direct questions.

This makes it difficult to assess:

• Your reasoning.
• Your trade-offs.
• Your debugging process.
• Your adaptability when given hints.

Communication is not a “soft” skill add-on. It is a core engineering competency. Engineering is inherently collaborative, and strong companies deeply value how you reason and articulate decisions.

You do not need to present perfect ideas. In fact, thinking out loud is often a positive signal.

For example, while solving this Priority-Based Data Fetching problem, saying:

“I’m thinking about using Promise.allSettled here, but I’m concerned it might not be optimal for very large inputs…”

That demonstrates structured thinking and awareness of trade-offs. It gives the interviewer context and creates opportunities for constructive guidance.

Silence, on the other hand, creates uncertainty.

Poor Code Quality

Some candidates solve the problem but write messy code.

Common issues include:

• Variable names like a, b, or temp1.
• Putting all logic into a single large function or file.
• No helper methods.
• Ignoring language-specific features.
• Skipping types when using TypeScript.

Some candidates justify this by saying they would write better code after joining the company.

But organizations do not hire based on promises of future performance. They hire based on what they observe during the interview.

Treat every solution as production-quality code.

Candidates who demonstrate clarity, discipline, and strong engineering judgment consistently leave a stronger impression and progress further.

Interviewers are not only evaluating correctness. They are evaluating whether they would be comfortable maintaining your code for the next five years.

We hope this helps.

---

If you are preparing for interviews or planning a switch in the near future, consider exploring devtools.tech. It provides access to 250+ company-tagged real interview questions, learning resources, a frontend system design guide, structured study plans, curated focus areas, and more.

Preparation done right compounds.

Note: If you would like to contribute to the platform (interview questions, resources, experiences, or anything else), please reach out to us here.