Praktika AI Toolchain Mastery For Engineers

How can AI write most of your code while you keep quality high?

The practical split is letting AI generate the bulk of the implementation while the engineer owns quality gates: code review, pull request checks, and UAT on anything an agent builds. Reaching a roughly ninety percent AI threshold depends less on the model and more on how tightly you anchor work in a shared context and verify the output before it merges. This course walks through both the generation and the control side of that workflow.

How do you use Claude Code CLI to make changes in a Flutter project?

You drive Flutter changes directly from the command line by giving Claude Code a clear task anchored in a context repository, then reviewing the diffs it proposes before applying them. Structuring the work with a plugin approach keeps larger changes organised instead of one sprawling prompt. Selecting the right Claude model for each task also matters, since heavier reasoning and quick edits have different needs.

How do you reduce token consumption when coding with AI assistants?

Token cost is driven by how much context you push into each request, so tightening what the assistant actually sees is the first lever. Routing calls through a proxy such as rtk can cut consumption further, and choosing a lighter Claude model for simpler tasks avoids paying for reasoning you do not need. These techniques are covered alongside the core setup of the coding assistants.

How do you run UAT on features built by AI agents?

Treat agent-built features like any untrusted contribution: gate the pull request with shared actions, then run user acceptance testing against the feature's intended behaviour rather than trusting that it works because it compiles. Observability tools like Datadog and the CMS help you investigate issues the tests surface. The idea is that the engineer stays the final checkpoint even when AI wrote most of the code.

How can engineers delegate tasks to AI agents through Slack?

Agents can pick up work handed to them in Slack, letting you orchestrate delivery without sitting inside the IDE for every change. This works best when it sits inside a wider pipeline where Cursor, Claude Code, and agents each handle the part they are suited to. Building that personal AI delivery pipeline, and scaling the practice across a team, is the endpoint of this course.