Design Patterns for AI Workflows¶
Design patterns for AI workflows are reusable templates and architectural approaches for structuring how researchers interact with AI tools — analogous to software design patterns.
Context & Background¶
As researchers gain experience with AI tools, common patterns emerge for structuring effective interactions:
- Prompt-Plan-Review-Revise (PPRR): Iterative cycle of planning and refinement
- DAAF (Data Analyst Augmentation Framework): Structured framework for AI-assisted data analysis
- Compilation and review: AI compiles information, human reviews and curates
- Skill-based architecture: Reusable AI instructions packaged as "skills"
- Pipeline pattern: Sequential processing stages with defined inputs and outputs
- Fan-out/gather: Parallel processing of independent tasks followed by synthesis
Practical Implications¶
- Learn the common patterns: Understanding established patterns saves you from reinventing them
- Match pattern to task: Different research tasks call for different interaction patterns
- Document your patterns: Write down workflows that work well so you and others can reuse them
- Evolve patterns over time: Refine your approaches based on what works