AI Workflows

AI workflows are structured approaches to using AI tools in research, moving beyond ad-hoc prompting to repeatable, reliable processes. They range from simple prompt-response chains to sophisticated multi-agent pipelines with human checkpoints.

Context & Background

The evolution from "using ChatGPT" to "building AI workflows" mirrors the broader maturation of AI adoption in academia. Early adopters discovered that reliable results require structured approaches — defining inputs, specifying expected outputs, building in verification steps, and creating reusable templates.

Common workflow patterns include:

• Prompt-Plan-Review-Revise (PPRR): A four-stage cycle for iterative refinement
• Pipeline workflows: Sequential processing stages (collect → clean → analyze → report)
• Fan-out/fan-in: Parallel processing of independent tasks with aggregation
• Human-in-the-loop: Automated steps with manual review at critical junctures

Key Perspectives

Multiple sources emphasize that workflow design is often more important than prompt engineering. A well-structured workflow with mediocre prompts will outperform brilliant prompts in a chaotic process.

Practical Implications

• Document your workflows: Write them down so they're reproducible and shareable
• Build incrementally: Start with manual steps, automate one at a time
• Include verification: Every workflow should have checkpoints for human review
• Use CLAUDE.md files: Encode workflow instructions in persistent configuration files
• Version your workflows: Track changes to workflow definitions alongside code

Related Concepts

• Agentic Workflows
• Plan Driven Development
• Design Patterns
• Human In The Loop