Overview

AI sessions degrade over time. Context fills with file reads, stack traces, and revised decisions, most of which aren't relevant to the current task. Understanding this gradient, recognizing when it's affecting output, and managing sessions accordingly is what separates productive multi-hour work from sessions that produce inconsistent results.

This lesson covers three things. First, how context windows actually work and what accumulates as a session grows. Second, the habits that keep sessions productive: one task per session, short validation loops, and the handoff pattern for carrying context forward without carrying noise. Third, the failure patterns that develop naturally in working sessions (kitchen sink sessions, correction loops, the trust-then-verify gap, and infinite exploration) and how to recognize and reset from each.

These habits apply to any AI-assisted development, but they're especially important in Dagster projects where sessions span asset creation, debugging, dbt models, and resource configuration across many files.

The examples in this lesson extend the pipeline beyond what we built in lessons 4 and 5, using a trending_events asset that pulls data from a news API as a hypothetical new integration. You won't build it yourself — it's there to illustrate session patterns in a realistic context.