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RUNLOCALAI · v38
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  5. /Capstone: Research AI System
  6. /Ch. 2
Capstone: Research AI System

02. Research Question

Chapter 2 of 18 · 10 min
KEY INSIGHT

A well-scoped research question is specific enough to answer within your timeline but general enough to generalize beyond your specific dataset. The research question anchors everything. It determines which baselines you compare against, which metrics you optimize for, and how reviewers will evaluate your contribution's significance. **Characteristics of Good Research Questions:** **Specificity:** "Can attention heads be replaced with linear projections?" is answerable. "Will transformers work better?" is not. **Measurability:** You must define a quantitative signal that indicates progress. This typically means a metric on a benchmark dataset. **Scope Control:** "Improving accuracy by 2% on ImageNet" is achievable in 3 months. "Solving protein folding" is not. **Example Transformation:** Weak: "How can we make language models more efficient?" Strong: "Can sparse mixture-of-expert routing with top-2 selection reduce FLOPs by 40% while maintaining within 1% accuracy on WMT'14 EN-DE?" **Failure Mode:** Many operators pick research questions that require proprietary data or compute resources they cannot access. Validate feasibility before committing. **Evaluating Question Quality:** | Dimension | Poor Question | Strong Question | |-----------|----------------|-----------------| | Specificity | "improve performance" | "reduce inference latency by 2x" | | Measurability | "intuitively better" | "BLEU score on standard split" | | Feasibility | "requires 1000 GPUs" | "runs on single A100" | | Novelty | "well-studied for 5 years" | " unexplored combination" |

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

EXERCISE

Refine your project proposal into a research question using the framework above. Explicitly state the metric and baseline you will use.

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Capstone Overview
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Related Work