RAG Pipeline Design & Vector Search
Retrieval-Augmented Generation is a cornerstone of modern AI engineering, and interviewers expect you to design robust, scalable retrieval systems. You must understand how to ingest unstructured data, chunk documents effectively, generate high-quality embeddings, and configure vector databases for low-latency similarity search. Strong performance involves anticipating bottlenecks such as context window limits, embedding drift, and retrieval noise.
Be ready to go over:
- Chunking strategies and semantic segmentation – How document structure impacts retrieval quality and context retention.
- Hybrid search architectures – Combining dense vector embeddings with sparse keyword search (BM25) using reciprocal rank fusion.
- Re-ranking mechanisms – Utilizing cross-encoder models to re-order retrieved passages for optimal relevance before context injection.
- Advanced concepts (less common) – Graph-based RAG topologies, hierarchical index structures, and learned sparse representations.
Example questions or scenarios:
- "Design a RAG pipeline that ingests millions of streaming financial reports, updates vector indices in near real-time, and guarantees sub-second retrieval latency."
- "How do you evaluate whether poor generation quality is caused by your retrieval step versus the underlying LLM's comprehension?"
LLM Evaluation & Guardrails
Evaluating non-deterministic generative models requires sophisticated methodologies that go beyond traditional software testing. You will be evaluated on your ability to build automated evaluation harnesses, implement LLM-as-a-judge frameworks, and establish robust safety guardrails. Strong candidates demonstrate a scientific approach to measuring model capabilities, regressions, and prompt robustness.
Be ready to go over:
- Automated evaluation harnesses – Creating test suites with deterministic assertions and semantic similarity checks for continuous integration.
- LLM-as-a-judge patterns – Designing rubric-based evaluation prompts while mitigating positional bias, verbosity bias, and self-enhancement effects.
- Red-teaming and safety guardrails – Proactively testing model boundaries against prompt injections, jailbreaks, and toxic outputs.
- Advanced concepts (less common) – Mechanistic interpretability metrics, automated constraint satisfaction verification, and behavioral clustering of failure modes.
Example questions or scenarios:
- "How would you build a regression test suite for a new model checkpoint to ensure it doesn't degrade on complex reasoning tasks while improving latency?"
- "Walk through how you would detect and neutralize a novel prompt injection vector targeting an enterprise customer service bot."
Multi-Agent Systems & Orchestration
Multi-agent workflows represent the cutting edge of applied AI, allowing autonomous systems to collaborate, execute code, and solve multi-step problems. Interviewers will test your understanding of agent loops, state management, tool use, and fault recovery. Success in this area requires balancing autonomy with strict execution safety.
Be ready to go over:
- Agent state management – Maintaining conversation history, scratchpads, and intermediate memory across asynchronous execution steps.
- Tool use and API integration – Designing robust schemas for model-driven tool invocation, error handling, and parameter validation.
- Safe code execution environments – Sandboxing agent-generated code execution using secure containers and timeout mechanisms.
- Advanced concepts (less common) – Hierarchical multi-agent delegation protocols, consensus-driven agent debates, and self-correcting feedback loops.
Example questions or scenarios:
- "Design an orchestration system for a team of coding agents that can independently clone a repository, write unit tests, debug failures, and open pull requests."
- "How do you handle infinite loops or catastrophic error cascades in a multi-agent workflow?"
System Design for LLM Serving
Serving frontier language models at scale presents unique infrastructure challenges, including high memory bandwidth requirements, dynamic batching, and strict latency SLOs. You must understand how to architect resilient inference serving layers that optimize hardware utilization and manage traffic surges gracefully.
Be ready to go over:
- Dynamic batching and KV caching – Maximizing GPU throughput by batching variable-length requests and managing key-value cache memory efficiently.
- Rate limiting and load shedding – Protecting downstream inference clusters from traffic spikes using token bucket algorithms and priority queues.
- Latency vs. throughput trade-offs – Configuring tensor parallelism, pipeline parallelism, and quantization levels for optimal serving performance.
- Advanced concepts (less common) – Speculative decoding pipelines, disaggregated serving architectures, and custom GPU memory allocators.
Example questions or scenarios:
- "Design an inference serving architecture that serves a 70B parameter model with a p99 latency target under 200 milliseconds."
- "How would you implement a fallback routing strategy across multiple frontier model providers during a regional infrastructure outage?"
