Hebbia Forward Deployed Engineer interview questions

Harvey's signature design question. The twist: the architecture is the easy half. The graded half is designing for reviewer trust, where one hallucinated clause costs more than a hundred correct ones earn.

Slides are the worst RAG corpus: text in shapes, meaning in charts, the message in the image. A query like 'the blue bar chart with Q3 revenue' fails if you embed only the extracted text, and citing the wrong slide number kills trust instantly.

A classic FDE take-home shape: messy real documents in, structured index out. The grade is in the seams between reader, extractor, and writer, and in what happens when one file is garbage.

You can't human-review every summary, and an LLM that wrote the summary can't be trusted to grade it. The move is span-level entailment against the transcript, then a threshold tuned to your cost-of-miss, not 0.5.

Five LLM agents over a hundred steps, and the bar is zero contradictions with a 500-page contract. The real test is the state and grounding layer, not the agent prompts.

The warm-up that quietly filters out candidates who only know LLMs as a magic API. Nail the two-phase inference picture and you set up every later answer about latency, cost, and hallucinations.

Everyone can define it; few candidates know why 'advertised context' and 'usable context' aren't the same thing. The cost, latency, and attention caveats that make this answer stand out.

The warm-up that filters out tutorial-level candidates. Interviewers aren't grading the diagram, they're grading whether you know the three places real pipelines fail and how you'd see it happening.

Everyone says '500 tokens with overlap.' The candidates who get hired explain why chunk boundaries are a recall problem, name three strategies, and describe the experiment that picks between them.

Modern LLMs share one architectural skeleton. Learn the five components and you can read almost any model card cold, plus the single distinction that proves to an interviewer you actually understand it rather than the buzzwords.

Tokens drive your customer's bill, their latency, and the weird failures like botched arithmetic. The rough numbers every FDE should rattle off, plus the trap answers interviewers listen for.

Every deployment has a skeptic, and 'I showed them data and they came around' is the answer everyone gives. The scored version starts with why they were right to be skeptical.

A deceptively simple sampling question with one trap (does temperature 0 fix hallucinations?) and one senior move interviewers reward. Know which workflows genuinely want zero.

Every FDE will face an angry customer asking this. The mechanistic explanation interviewers want (why hallucination is the default behavior, not a bug) plus the framing that defuses customer escalations.

The single most common FDE behavioral question, and the 'week one' follow-up is where most candidates collapse. Here's the structure that signals you can be dropped into chaos and produce order.

JSON mode, strict schemas, constrained decoding, validate-and-retry: the escalation ladder for structured output, plus the catch nobody mentions: schema-valid can still be factually wrong.

The retrieval design question with a precise rubric: candidates who can say what each stage rescues, and what it costs in latency, clear it. Includes the RRF detail and the latency budget interviewers ask for.

This is a flight-risk filter, not small talk, FDE teams lose hires back to pure SWE roles constantly. The answer that passes is built on evidence, not enthusiasm. Here's what it sounds like.

The concept underneath every RAG system you'll ever deploy. How to explain vectors, cosine similarity, and the one limitation of embeddings that costs real deployments accuracy.

The most common screen opener, and interviewers use it to check whether hashmap thinking is reflexive. Here's the one-pass answer, the narration that earns points, and the duplicate-handling edge case most candidates fumble.

The same operation scores attention and ranks your RAG results. Knowing exactly what the dot product measures, and when its magnitude term quietly breaks your retrieval, is what separates a working pipeline from a mysteriously bad one.