Technical interview questions for 2026

Technical interviews in 2026 are all about practical skills and modern workflows. Here’s what you need to know:

• AI Collaboration: 82% of developers use AI tools daily. Interviews now test how well you work with AI, focusing on prompt engineering, debugging, and validating AI outputs.

• System Design: Expect questions on modern architectures like RAG pipelines, serverless patterns, and fault tolerance. Cloud-native systems and observability tools like OpenTelemetry are critical.

• Debugging Skills: Companies prioritize structured approaches to troubleshooting, using frameworks like SRE-STAR(M) and focusing on metrics, traces, and logs.

• Cross-Team Communication: You’ll be evaluated on your ability to explain technical decisions, handle ambiguity, and collaborate with non-engineering teams.

Interviews have shifted from abstract puzzles to tasks that reflect everyday development work. Mastering these areas is now key to standing out.

::: @figure {Technical Interview Trends 2026: Key Statistics and Focus Areas}

1. AI-assisted coding and problem solving

The days of solving abstract puzzles during coding interviews are fading fast. With 97% of developers now using AI assistants , the focus has shifted. Instead of testing memory for algorithms, interviews now evaluate how well candidates collaborate with AI tools by testing both technical and soft skills.

Prompt engineering and iterative refinement

Crafting effective prompts has become just as important as writing clean, efficient code. Recognizing this, HackerRank introduced seven new prompt engineering questions in January 2025 . These questions test candidates on their ability to set the right context, define constraints, and refine AI outputs. The goal? To see if they can guide AI through multi-step conversations, correct its errors, and optimize its suggestions instead of blindly accepting them. This skill naturally ties into another critical task: validating AI-generated outputs.

AI-generated code validation

Once candidates master prompt engineering, the next step is ensuring the AI's output is correct. Modern interviews now include questions that test the ability to vet technical skills by spotting issues like outdated libraries, inefficient coding patterns, or security risks such as prompt injection . Exceptional engineers don’t just trust AI outputs - they dig deeper, validating and improving them with a solid understanding of the underlying logic.

Real-world debugging with AI assistance

Gone are the days of solving simple coding puzzles. Today’s interviews present candidates with messy, buggy codebases and ask them to debug using AI tools . For example, OpenAI’s interview rounds now include tasks like implementing a fully functional resumable iterator . These exercises simulate real-world scenarios where developers work on extending and improving existing codebases with AI as their partner.

Monitoring the collaboration pattern

The focus isn’t just on the final code anymore - it’s on the process. By reviewing AI interaction logs , interviewers can see if candidates created original, thoughtful prompts and worked effectively with AI. For instance, Red Hat’s project-based assessments in 2025 disqualified 63% of first-phase candidates who lacked practical AI collaboration skills. This new approach significantly shortened their hiring process .

The trend is clear: mastering AI collaboration is no longer optional - it’s the new standard for developers.

2. System design for modern architectures

System design interviews have evolved significantly, especially with the rise of AI-driven solutions. In 2026, candidates are expected to showcase a strong grasp of modern architectural principles, including the trade-offs, complexities, and emerging patterns shaping today's software landscape. Fahim ul Haq, CEO of Educative, highlights this shift:

"System Design interviews have become far more structured and demanding, especially as modern software increasingly adopts AI-Agentic architectures."

This shift has made it critical for candidates to understand key strategies and technical decisions that drive these systems.

RAG Pipelines and Vector Databases

Retrieval-Augmented Generation (RAG) architectures are becoming the go-to choice for many companies, offering better cost management and easier maintenance compared to custom model fine-tuning. Interviewers now often test candidates on how to design systems that combine large language models with vector databases like Pinecone, Milvus, or Qdrant for tasks such as similarity searches and context retrieval . Additionally, you may be asked to outline methods for implementing content moderation - both for user inputs and model outputs - to minimize hallucinations and ensure compliance with policies.

Serverless and Edge Computing Patterns

A strong command of event-driven architectures is another must-have skill. This includes working with tools like AWS Lambda, API Gateway, and DynamoDB. Familiarity with orchestration patterns, such as the Saga pattern for managing distributed transactions, is often tested. You should also be ready to discuss strategies to optimize cold starts, such as using lighter runtime environments, reducing dependencies, and leveraging provisioned concurrency to maintain consistent response times .

Fault Tolerance and Availability Trade-offs

Understanding the SPARCS framework - Scalability, Performance, Availability, Reliability, Consistency, and Security - is essential for tackling fault tolerance and availability challenges . For instance, Netflix relies on NoSQL databases to handle the scalability demands of over 1 billion weekly streaming hours . Candidates should also be prepared to explain fault tolerance techniques like circuit breakers, retry logic with exponential backoff, and dead-letter queues to mitigate cascading failures . Moreover, understanding the concept of availability "nines" is a key metric; for example, achieving 99.999% uptime means just 5.26 minutes of downtime per year .

Distributed Observability and Monitoring

As systems grow increasingly distributed, robust observability practices are becoming a critical part of system design interviews. Candidates should demonstrate knowledge of distributed tracing, health checks, and structured logging. Steve Briskin, VP of Engineering at Viam, underscores the importance of this awareness:

"Anyone who's 100% on the microservices train is a hard no from me. I look for awareness of issues with code, release management, permissions, operations, APIs."

With AI-powered observability tools becoming standard, you should also be ready to discuss how machine learning can address challenges like cardinality explosion or automate threshold detection in metrics pipelines . These skills are increasingly valued as they ensure systems remain resilient and manageable in complex environments.

3. Real-world debugging and optimization

While AI tools have simplified many coding tasks, the ability to manually troubleshoot and fine-tune production issues is still critical. By 2026, technical interviews are placing a strong emphasis on how candidates approach diagnosing production problems. Companies want to see if you can identify issues systematically rather than relying on guesswork. This involves understanding key concepts like monitoring versus observability. Such diagnostic skills align with the broader shift toward solving practical, real-world challenges.

Structured Diagnostic Frameworks

Interviewers often evaluate your problem-solving approach using frameworks like SRE-STAR(M): Symptom → Triage → Assess → Root Cause → Mitigation . It’s crucial to show that you can prioritize immediate actions during a crisis - such as rolling back a problematic deployment or restarting a service . As one expert put it:

"Symptom-based alerting is strongly preferred. An alert should only fire if users are being measurably affected." – Second Talent

This approach ensures that your efforts are focused on minimizing user impact while addressing the root cause effectively.

The Three Pillars of Observability

A strong grasp of observability is essential for structured diagnostics. Effective observability revolves around three main pillars: metrics, traces, and logs. These tools help detect, isolate, and understand system failures. For instance, using the RED method (Rate, Errors, Duration) for microservices can be an effective strategy . Additionally, understanding service-level objectives (SLOs) - like knowing that a 99.9% availability SLO allows for about 43 minutes of downtime per month - adds context to the urgency of optimization efforts .

High-Cardinality Data and Correlation

Debugging modern systems often involves managing high-cardinality data. This means handling datasets with numerous unique values, such as user IDs or session IDs. Techniques like using correlation IDs and structured JSON logging (with sensitive data properly redacted) are key skills to demonstrate .

Emerging Tools and Techniques

Candidates are increasingly expected to be familiar with cutting-edge tools and methods. Topics like eBPF, continuous profiling, and OpenTelemetry have gained prominence. These tools reflect a shift in monitoring practices, such as moving from head-based to tail-based sampling, which provides a more comprehensive view of system performance [27,28].

4. Cross-functional collaboration and culture

These days, it's not enough to just be technically skilled. Companies are increasingly looking for candidates who can seamlessly work across diverse teams. By 2026, technical expertise needs to go hand-in-hand with the ability to collaborate effectively with product managers, designers, and other non-engineering colleagues. Interviews are evolving to test how well you can integrate into these cross-functional environments.

Business Justification and Stakeholder Communication

Understanding the "why" behind your work has become just as important as knowing the "how." Interviewers want to see if you can connect technical decisions to broader business goals. For example, you might encounter questions like, "How did you balance technical debt with urgent business needs from product management?" . These types of questions aim to uncover whether you can translate technical constraints into business value. Atlassian's Engineering Interview Guide puts it this way:

"We want to understand how you explore an issue - what questions you would ask, how you'd talk through constraints like reliability and cost, who you might partner with for help, and which technologies you'd use."

This focus on business context highlights your ability to collaborate in real-world scenarios.

Handling Ambiguity and Building Consensus

Interviews today often include intentionally vague requirements to see how you handle uncertainty. The goal is to assess whether you ask thoughtful, clarifying questions before diving into code. You'll likely face situations where the requirements shift mid-discussion, testing your ability to adapt and remain flexible . Fahim ul Haq, CEO of Educative, emphasizes this point:

"Your ability to clearly explain your reasoning and adapt when requirements change is just as important as the technical solution itself."

This aligns closely with the importance of transparency in technical decision-making, a skill that’s becoming increasingly valued.

Code Review and Communication Under Pressure

Some interviews simulate real-world team workflows by asking you to review a pull request containing subtle issues. At the same time, you might be required to communicate updates to stakeholders under tight deadlines . These scenarios test not just your technical eye but also your ability to stay composed and articulate under pressure. Ultimately, the strongest indicator of success is whether the interviewer feels they'd want to collaborate with you on a tough problem .

Conclusion

Traditional interviews are increasingly out of step with the realities of modern developer work. As highlighted in the HackerRank 2025 Developer Skills Report:

"The disconnect between traditional interview methods and actual developer work has never been more pronounced" .

With 82% of developers now using AI tools and 66% favoring practical coding challenges over abstract puzzles, it's clear that the way we evaluate technical talent needs to evolve .

Forward-thinking companies are shifting to assessments that emphasize real-world skills like designing resilient system architectures, effective collaboration, and practical tool usage. Project-based evaluations have emerged as a powerful way to streamline hiring while improving the quality of candidates .

These evaluations focus on tasks that mirror everyday developer responsibilities: reviewing pull requests, debugging existing codebases, designing systems within real-world constraints, and explaining technical trade-offs to non-technical team members. Unlike outdated whiteboard puzzles, these practical assessments offer a more accurate measure of job performance. They also provide a fairer, more engaging candidate experience, which helps strengthen your company’s tech brand. By embracing modern practices - such as AI-assisted workflows, system design under real-world conditions, detailed debugging, and cross-functional collaboration - you redefine how technical talent is assessed.

With daily.dev Recruiter, you can connect with pre-qualified developers through warm, double opt-in introductions. By aligning your interview process with how developers actually work - leveraging AI tools, navigating complex challenges, and collaborating across teams - you’re not just speeding up hiring; you’re building teams of innovative problem-solvers ready to meet the demands of today’s software engineering landscape.

The era of modern evaluations, designed to reflect real-world tasks, is here. Adapting quickly is essential to securing the best talent.

FAQs

How can I improve my AI collaboration skills for technical interviews in 2026?

To sharpen your AI collaboration skills, approach AI tools as if they are teammates, not just shortcuts. In 2026, technical interviews emphasize how effectively you work with AI tools like GitHub Copilot or similar assistants to tackle problems. It's not just about how fast you code - it’s about how well you clarify requirements, break down problems, validate AI-generated outputs, and explain your reasoning while collaborating with AI.

The best way to prepare? Practice regularly with the AI tools you’re likely to encounter in interviews. Engage in coding challenges, craft clear prompts, review AI-generated suggestions, and carefully verify their accuracy. Treat these exercises like pair programming sessions: narrate your thought process, ask questions to clarify ambiguities, and explain why you accept or reject AI recommendations. Include tasks like prompt engineering, such as defining constraints or testing edge cases, and simulate real-world problem-solving scenarios.

After each session, take time to reflect. What mistakes did the AI make? How did you identify and correct them? What was your reasoning? This consistent practice will help you show the combination of technical skills and AI collaboration that interviewers are now looking for.

What are RAG pipelines, and why are they significant in modern system design?

Retrieval-Augmented Generation (RAG) pipelines bring together two key components: a retriever and a generator. The retriever searches external knowledge sources, such as databases or APIs, to pull relevant information. The generator, often a large language model (LLM), then uses this data to craft responses that are not only fluent but also rooted in factual, real-world information. This combination helps address the inaccuracies that can occur when relying solely on generative models.

RAG pipelines play a crucial role in system design by offering flexible and scalable architectures. Separating the knowledge base from the model means engineers can update data without needing to retrain the model. Additionally, performance can be fine-tuned using specialized retrieval techniques, and reliability can be tracked with separate metrics for both retrieval and generation. These advantages make RAG pipelines a natural fit for applications like chatbots, enterprise search tools, and recommendation systems, where precision and adaptability are key.

How can I showcase strong cross-team communication skills during an interview?

To showcase your cross-team communication abilities, consider describing a project where you served as the bridge between technical and non-technical teams. For instance, you might detail a scenario where you worked closely with engineering, product, and design teams to deliver a critical feature or improve a specific metric.

Start by outlining the Situation - what was the project about, and why was it important? Then, explain your Task - what role did you play in ensuring smooth collaboration? Highlight the Actions you took, such as organizing regular check-ins, using tools like Slack or shared dashboards to keep everyone aligned, or translating technical jargon into language that non-technical stakeholders could easily understand. Finally, share the Result, focusing on measurable outcomes like delivering the project ahead of schedule, reducing errors, or boosting user engagement.

Make sure to emphasize how you clarified requirements early on, proactively identified risks, and ensured feedback from non-technical colleagues was incorporated into the process. If you introduced any collaboration techniques, such as cross-functional meetings or shared objectives, mention those. Additionally, describe how you handled disagreements or misalignments constructively - perhaps by mediating discussions or finding compromises that balanced technical feasibility with stakeholder needs.

By structuring your example this way, you not only demonstrate your ability to align diverse teams but also show how your efforts directly contributed to achieving project goals.