I went pretraining to finetuning in a pretty linear way, next-token prediction, massive corpora, then supervised finetuning on labeled data.
Start by framing the two-stage process: pretraining builds general language understanding from massive unlabeled text, while finetuning adapts that general model to a specific task or domain. Then walk through each stage step-by-step, highlighting key architectural components, objectives, and trade-offs. Finally, connect it to practical considerations like compute cost, data requirements, and evaluation.
Pro tip: Emphasize that finetuning is not just about task performance—it's also about aligning the model with human preferences (e.g., RLHF) and mitigating harmful biases, which is critical for production systems at companies like Samsara.
Explain that pretraining uses a large corpus of unlabeled text (e.g., web pages, books) and a self-supervised objective like masked language modeling (BERT-style) or causal language modeling (GPT-style). Mention that the model learns general linguistic patterns and world knowledge.
Describe the transformer architecture (self-attention, feed-forward layers, layer norm) and how scaling model size, data, and compute leads to emergent abilities. Note that pretraining is computationally expensive and typically done once by large labs.
Explain that finetuning takes the pretrained model and continues training on a smaller labeled dataset for a specific task (e.g., classification, summarization). Mention techniques like full finetuning, parameter-efficient methods (LoRA, adapters), and prompt tuning.
Discuss how instruction tuning and reinforcement learning from human feedback (RLHF) further align the model to follow instructions and generate helpful, harmless responses. This is often the final step before deployment.
Highlight trade-offs: pretraining is costly but general; finetuning is cheaper but can overfit or forget. Mention evaluation, catastrophic forgetting, and the importance of data quality and compute budget.
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This is where the conversation got interesting.
Start by defining both methods clearly, then compare them across dimensions like data requirements, computational cost, and alignment effectiveness. Emphasize that they are complementary, not mutually exclusive, and discuss how they are often used together in modern pipelines.
Pro tip: Mention that RLHF typically requires an initial instruction tuning step, so the real tradeoff is about when to stop instruction tuning and add RLHF. Also highlight that RLHF can optimize for nuanced human preferences but introduces complexity and potential reward hacking.
Explain that instruction tuning fine-tunes a pretrained model on a dataset of instruction-response pairs to improve zero-shot and few-shot performance on new tasks.
Describe RLHF as a three-step process: collecting human preference data, training a reward model, and fine-tuning the language model with reinforcement learning to maximize the reward.
Discuss tradeoffs in terms of data efficiency, computational cost, alignment quality, and risk of reward hacking. Instruction tuning is simpler and cheaper but may not capture complex preferences; RLHF is more powerful but resource-intensive and harder to stabilize.
Explain that in practice, instruction tuning is often a prerequisite for RLHF, and the two can be combined to leverage the strengths of both.
Connect the discussion to how companies like Samsara might use these techniques for domain-specific alignment, considering constraints like latency, cost, and safety.
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Went with retrieval augmentation, better decoding configs, and grounding outputs against structured sources.
Start by defining what hallucinations mean in the context of Samsara's products, then structure your answer around prevention, detection, and mitigation strategies. Emphasize trade-offs between accuracy, latency, and cost, and tie your approach to real-world deployment constraints.
Pro tip: Acknowledge that eliminating hallucinations entirely is unrealistic; instead, focus on measuring and bounding them with fallback mechanisms and human-in-the-loop for high-stakes decisions. This shows you understand production ML systems, not just research.
Clarify what constitutes a hallucination for the specific use case (e.g., factual errors, fabricated citations) and the acceptable risk level. Consider Samsara's domain (IoT, fleet safety) where incorrect information could have safety implications.
Discuss techniques to reduce the likelihood of hallucinations upfront, such as retrieval-augmented generation (RAG), fine-tuning on domain-specific data, and prompt engineering with constraints.
Explain how to detect hallucinations in real-time (e.g., consistency checks, uncertainty estimation) and mitigate them (e.g., fallback to retrieval, asking for clarification, or refusing to answer).
Describe metrics (e.g., factual accuracy, hallucination rate) and continuous monitoring in production, including A/B testing and user feedback loops.
Discuss trade-offs between accuracy, latency, cost, and user experience, and how to balance them in a production system.
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Perplexity is easy to talk about but I always feel like interviewers want you to say it's insufficient on its own, which it is.
Start by framing evaluation as a multi-layered process that depends on the use case, then walk through intrinsic metrics (perplexity), extrinsic task-specific metrics (accuracy, F1, etc.), and human evaluation, explaining when each is appropriate. Emphasize that no single metric is sufficient and that the choice should align with business goals and deployment constraints.
Pro tip: Mention that perplexity is useful for comparing language models on the same tokenization and dataset, but it can be misleading for open-ended generation—so always pair it with task-specific and human evaluations. Also, highlight the importance of establishing a golden test set and using statistical significance testing when comparing models.
Clarify what the LLM will be used for (e.g., classification, generation, retrieval-augmented QA) and what success looks like for the business. This determines which metrics matter most.
Explain that perplexity measures how well the model predicts a sample and is useful for comparing models on the same data, but it doesn't capture task performance or generation quality.
For classification, use accuracy, precision, recall, F1; for generation, use BLEU, ROUGE, METEOR, or BERTScore; for QA, use exact match and F1. Always evaluate on a held-out test set.
Design human evaluation protocols (e.g., Likert scales, pairwise comparisons) to assess fluency, coherence, relevance, and factuality, especially for open-ended tasks where automatic metrics fall short.
Use a dashboard or scorecard to track multiple metrics, perform error analysis, and run A/B tests in production to measure real-world impact. Continuously refine based on feedback.
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Drift detection, output quality sampling, latency tracking.
Start by framing monitoring as a multi-layered system covering performance, quality, cost, and safety. Then walk through concrete metrics, tools, and processes for each layer, emphasizing proactive detection and continuous improvement. Finally, tie it back to Samsara's IoT/edge context by discussing latency, data drift, and integration with existing observability stacks.
Pro tip: Don't just list metrics—explain how you'd set thresholds and alerts based on business impact, and how you'd use shadow deployments and canary releases to validate changes before full rollout.
Identify what matters for the LLM application: latency, accuracy, cost per query, safety, and user satisfaction. Translate these into measurable SLIs and set SLOs with error budgets.
Log inputs, outputs, latency, token usage, and model metadata. Use distributed tracing to capture end-to-end request flows and integrate with existing observability tools like Prometheus, Grafana, or Datadog.
Track latency percentiles, throughput, error rates, hallucination rates, toxicity, and cost per token. Use automated evaluations, user feedback, and anomaly detection to catch issues.
Define alert thresholds based on SLOs and business impact. Create runbooks for common failure modes (e.g., model drift, API outages) and integrate with on-call systems.
Regularly review monitoring data to retrain or fine-tune models, update prompts, and adjust thresholds. Use A/B testing and shadow deployments to safely roll out improvements.
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Start by clarifying the production constraints (latency, throughput, cost, hardware) and then walk through optimizations across the inference stack: model-level (quantization, pruning, distillation), runtime-level (KV caching, batching, paged attention), and system-level (caching, routing, autoscaling). Emphasize trade-offs and how you would measure impact with metrics like TTFT, TPOT, and cost per token.
Pro tip: Always tie optimizations to business metrics and user experience—e.g., 'Quantization reduced latency by 30% but required careful calibration to avoid accuracy drop on edge cases.' This shows you think beyond just technical implementation.
Ask about latency SLOs, throughput targets, cost budget, hardware (GPU/CPU, memory), and accuracy tolerance. This ensures your optimizations are relevant and prioritized.
Discuss quantization (INT8, FP16, GPTQ, AWQ), pruning, knowledge distillation, and weight sharing. Mention trade-offs: reduced memory and compute vs. potential accuracy loss.
Cover KV caching, continuous batching, paged attention (vLLM), speculative decoding, and optimized kernels (FlashAttention). Explain how these reduce latency and increase throughput.
Include caching (prompt/response), request routing, autoscaling, model parallelism (tensor/pipeline), and hardware selection (e.g., A100 vs. H100). Highlight cost and scalability benefits.
Define metrics (TTFT, TPOT, throughput, cost per token, accuracy) and A/B test optimizations. Monitor for regressions and adjust based on production feedback.
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