LESSON 8.3 · Diagnosis · 120 min
Long context is not one number
Long context is not one number. Learn sparse attention, retrieval, positions, context rot, needles, and structured evaluation and complete: Build a layered evaluation from retrieva
Learning objectives
- Explain what problem “Long context is not one number” solves without hiding behind terminology.
- Trace the variables and causal links across sparse attention, retrieval, positions.
- Complete “Build a layered evaluation from retrieval to multi-hop reasoning” and judge the result with evidence rather than intuition.
Core concepts
sparse attention
sparse attention is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment before moving to an optimized implementation.
retrieval
retrieval is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
positions
positions is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
context rot
context rot is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
needles
needles is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
Build and verify
Build a layered evaluation from retrieval to multi-hop reasoning
- Predict: write the expected output, trend, or failure before running code.
- Build: implement only the minimum components needed to answer the question.
- Verify: compare with a baseline or trusted implementation; save seeds, parameters, and raw outputs.
- Transfer: change one shape, dataset, scale, or workload condition and explain whether the conclusion still holds.