PLACEHOLDER

Step 1: Error Analysis First (Not Feature Development)

Start every sprint by reviewing actual AI failures, not building new features. Create a simple log: input → AI output → expected output → failure type. Most teams skip this and wonder why their AI gets worse over time.

Step 2: Build Your AI "Flight Recorder"

Create a lightweight viewer to inspect every AI interaction with full context (user input, prompt, model response, relevant data). This isn't a dashboard—it's your debugging lifeline when things go wrong.

Step 3: Synthetic Data for Rapid Testing

Generate test cases across 3 dimensions: features (what the AI does), scenarios (edge cases), and personas (user types). Use LLMs to create realistic test data before you have real users. Prevents the "no users = no data" trap.

Step 4: Domain Experts Own Prompts

Give non-technical team members direct access to modify prompts in a safe environment. They understand the "what" better than engineers understand the "how." Eliminates the translation layer that kills iteration speed.

Step 5: Experiment-Driven Roadmap

Replace feature roadmaps with experiment logs. Each experiment = hypothesis + measurement plan + success criteria. Track experiments run, not features shipped. This is how you maintain velocity when outputs are unpredictable.

Step 6: Evaluation Guardrails

Build systems to prevent "criteria drift"—when your definition of "good output" changes as you see more examples. Create clear rubrics, regular recalibration sessions, and audit processes to maintain trust in your measurements.