I went straight to chunking and retrieval augmentation, which felt safe but maybe too surface level.
Start by clarifying the constraints: model type, context window size, latency and cost requirements, and whether the task needs full-document understanding or just relevant snippets. Then propose a layered solution that combines input reduction (chunking, retrieval, summarization) with architectural adaptations (sliding window, sparse attention, memory mechanisms), and discuss trade-offs for each approach.
Pro tip: Emphasize that the best solution depends on the task: for QA, retrieval-augmented generation (RAG) often suffices; for summarization, hierarchical chunking works well; for long-form generation, memory mechanisms or recurrence may be needed. Show you can adapt to constraints rather than defaulting to one method.
Ask about the model architecture, context window size, input length distribution, latency and cost budgets, and the specific task (e.g., QA, summarization, generation). This ensures your solution is tailored.
Discuss methods like chunking with overlap, retrieval-based selection of relevant passages, or summarization of chunks before feeding to the model. These reduce input length while preserving key information.
Mention approaches like sliding window attention, sparse attention (e.g., Longformer, BigBird), recurrence, or memory mechanisms (e.g., Transformer-XL, compressive memory) that extend effective context without retraining from scratch.
Compare methods on accuracy, latency, cost, and implementation complexity. Often a hybrid (e.g., RAG + chunking) works best. Justify your choice based on the clarified requirements.
Propose metrics (e.g., task-specific accuracy, latency, cost) and a strategy to test different approaches, such as A/B testing or offline evaluation, to ensure the solution meets production needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.