LLM & GenAI Fundamentals is a core part of the Mistral 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 Mistral first, then the highest-signal questions from our LLM & GenAI Fundamentals track, each with an answer written to a senior-engineer bar.
LLM & GenAI Fundamentals questions tagged to Mistral
More LLM & GenAI Fundamentals questions for Mistral's loop
The highest-signal llm & genai fundamentals questions candidates rate most useful, modeled on what Mistral's Forward Deployed Engineer loop tests.
Concepts behind Mistral'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.
Mistral's Forward Deployed Engineer loop draws llm & genai fundamentals questions such as "What is a token, and why should an FDE care about tokenization?", "What's the difference between a base model and an instruct model, and why would a customer care?", "When does fine-tuning beat few-shot prompting? How much data do you need, and how do you prevent regressions?". 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 Mistral's full interview process for how this round fits the loop.
Yes. Mistral hires Forward Deployed AI Engineers and Forward Deployed Machine Learning Engineers within its Applied AI team, working with customers from pre-sale through production, often for sovereign and on-premise deployments.
Expect strong Python, LLM internals such as quantization, serving, and fine-tuning, and use-case design for on-prem or data-sovereign RAG and agent deployments, plus customer-facing scoping. Depth on running open-weight models yourself matters.
Other Mistral interview rounds
The other tracks Mistral's Forward Deployed Engineer loop tests.
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