LLM & GenAI Fundamentals is a core part of the Scale AI Forward Deployed Engineer loop. Tokens, context windows, prompting vs RAG vs fine-tuning, hallucinations, evals, cost and latency: the conceptual core every AI-native FDE loop probes first. Below are the llm & genai fundamentals questions to prepare, the ones tagged to Scale AI first, then the highest-signal questions from our LLM & GenAI Fundamentals track, each with an answer written to a senior-engineer bar.
WHAT SCALE AI LOOKS FOR HERE · Enterprise, government, and defense deployments. See the full Scale AI interview process →
LLM & GenAI Fundamentals questions tagged to Scale AI
More LLM & GenAI Fundamentals questions for Scale AI's loop
The highest-signal llm & genai fundamentals questions candidates rate most useful, modeled on what Scale AI's Forward Deployed Engineer loop tests.
Concepts behind Scale AI's LLM & GenAI Fundamentals round
The vocabulary and mental models these questions assume. Start with the foundations free; the deeper, interview-defining ideas are part of premium.
FoundationalTokenization & TokensA language model does not read characters or words. It reads tokens: sub-word chunks produced by a tokenizer, each mapped to an integer the model embeds. Tokens are the unit of the context window and of billing, and the way text splits into them explains a surprising number of model quirks, which is why almost every loop opens here.
FoundationalThe Context WindowThe context window is the fixed number of tokens a language model can attend to at once, and input and output share that same budget. Understanding it is what separates engineers who can size a prompt, control cost and latency, and decide when to reach for RAG from those who just paste everything in and hope.
FoundationalEmbeddings & Vector RepresentationsAn embedding turns a piece of text into a list of numbers positioned so that similar meanings land near each other in space, which lets you search by meaning instead of by keyword. Embeddings are the engine under RAG, semantic search, clustering, and deduplication, so FDE loops expect you to explain cosine similarity and the pitfalls that quietly break a vector index.
FoundationalThe Transformer, IntuitivelyThe transformer is the architecture behind every modern large language model, built on self-attention that lets each token look at every other token in parallel. FDE loops do not want the math; they want you to explain why attention beat RNNs, what decoder-only means, and why context length is expensive, in plain language an exec or a teammate can follow.
CoreSign inAttention and Self-AttentionAttention computes a weighted sum of value vectors, where the weights come from how well each token's query matches every other token's key. Self-attention applies this within one sequence so each token can pull from all the others, and the all-pairs comparison is why cost grows with the square of sequence length. FDE loops probe it because it explains context limits, latency, and the KV cache in one mental model.
CoreSign inRoPE and Positional EncodingsSelf-attention has no built-in sense of word order, so transformers inject position information into the token vectors. Rotary position embeddings (RoPE) rotate query and key vectors by a position-dependent angle so relative position falls out of the dot product, which is why RoPE underpins almost every long-context model and why extending a context window means rescaling RoPE.
FoundationalTemperature, Top-p and SamplingAt each step a model outputs a probability over every possible next token, and sampling settings like temperature, top-p, and top-k decide how that distribution is turned into an actual choice. FDE loops test this because it controls the determinism-versus-creativity dial: knowing when to set it low for extraction and high for brainstorming, and why the same prompt giving different answers is expected, not a bug.
Advanced🔒 PremiumLoRA and Parameter-Efficient Fine-tuningFull fine-tuning updates every weight in a model, which is expensive to train and produces a full-size checkpoint per task. LoRA freezes the base model and trains small low-rank adapter matrices instead, giving tiny swappable checkpoints; QLoRA adds a quantized frozen base so the whole thing fits on a single GPU. FDE loops probe it because it is how you adapt a model on a customer's data without their budget or their hardware blowing up.
Scale AI's Forward Deployed Engineer loop draws llm & genai fundamentals questions such as "Why do LLMs hallucinate?", "You're building a classifier that flags risky contract clauses. Do you optimize for precision or recall?", "A customer wants the model to 'know our docs.' Prompting, RAG, or fine-tuning: how do you choose?". Tokens, context windows, prompting vs RAG vs fine-tuning, hallucinations, evals, cost and latency: the conceptual core every AI-native FDE loop probes first. The full set, ordered easy to hard with expert answers, is below.
Work the llm & genai fundamentals questions on this page in order, study the underlying concepts linked below, then review Scale AI's full interview process for how this round fits the loop. For Scale AI specifically, interviewers weight this: Enterprise, government, and defense deployments.
Yes. Scale AI hires Forward Deployed and Senior Forward Deployed AI Engineers who work with enterprise, defense, and government customers to deliver data and GenAI systems.
Expect a recruiter screen, engineer screens, and live coding, plus discussion of GenAI deployment and the data infrastructure behind model development. The process usually runs several rounds. Candidate reports from early 2026 describe a recruiter call followed by a 45-minute live-coding round with an engineer, expanding to three or four rounds and six or seven interviews over roughly three weeks.
Other Scale AI interview rounds
The other tracks Scale AI's Forward Deployed Engineer loop tests.
Prep the whole Scale AI Forward Deployed Engineer loop
LLM & GenAI Fundamentals is one round. Unlock every answer across Scale AI's full loop with Premium. Free questions in every track let you try the depth first.
Every answer, concept and course, every paper breakdown, all published video lessons, hands-on FDE Lab missions, Premium PDF guides and companion files, the full practice-test bank and work-sample downloads. Referral Premium excludes guide PDFs and their companion files.
6 months · One payment · No auto-renewal
Study alongside free video lessons.
Independent and not affiliated with Scale AI. All trademarks belong to their owners.