Full course coming soon

Building Validation Pipelines for AI-Assisted Research Workflows

7 chapters · 33 lessons

1. 1. Candidate Generation vs. Knowledge Production

   4 lessons

   Understand the fundamental distinction between AI-generated outputs and validated research, and map the validation gap in your domain.

2. 2. Provenance Tracking Systems

   5 lessons

   Build systems that trace every AI-generated claim, code snippet, and hypothesis back to verifiable sources.

3. 3. Falsifiability and Validation Layers

   5 lessons

   Implement concrete checks that test whether AI-generated research candidates can be challenged, reproduced, or formalized.

4. 4. Role Clarity and Accountability Frameworks

   4 lessons

   Establish who decides when an AI-assisted output is correct enough to act on, and how responsibility flows through the pipeline.

5. 5. Technical Research Workflows

   5 lessons

   Apply validation pipelines to literature mapping, proof assistance, baseline code generation, and computational experiments.

6. 6. Product and Business Research Workflows

   5 lessons

   Build validation systems for customer feedback analysis, market narrative synthesis, and evidence-based decision making.

7. 7. Engineering and System Design Workflows

   5 lessons

   Validate AI-generated design alternatives, test cases, failure modes, and architectural recommendations in engineering contexts.