Task-Specific Optimization Over Generic Tuning
The Principle
No single configuration performs best across different tasks. Effective optimization requires adapting to the specific characteristics of each use case rather than searching for universal defaults.
Why This Matters
The pattern I keep encountering in complex retrieval systems: gains from systematic tuning are consistent but not uniform. What works for one benchmark degrades on another. High-performing configurations share some parameters but diverge on others.
This means two things for practitioners. First, default configurations leave significant performance on the table, often 60%+ improvement is available through tuning alone. Second, that tuning effort needs to be repeated for each substantially different task.
Generic “best practices” for chunk size or retrieval method may not apply to your specific domain.
How to Apply
- Treat configuration as a first-class design decision, not an afterthought
- Budget time for task-specific tuning when deploying to new domains
- Don’t assume settings that worked elsewhere will transfer
- Build evaluation pipelines that let you systematically explore the configuration space
When This Especially Matters
- Multi-hop reasoning tasks where configuration choices compound
- Domain-specific applications where benchmarks don’t exist
- Any system where “good enough” defaults aren’t actually good enough
Limitations
Task-specific tuning requires task-specific evaluation data. If you can’t measure it, you can’t tune for it. This creates a chicken-and-egg problem for novel applications.
Related: 05-atom—configuration-sensitivity-modular-ai, 03-atom—evaluation-metric-mismatch-qa, 05-molecule—rag-architecture-taxonomy