AI-Assisted Workflows
Working effectively with an AI coding agent is its own skill: plan before you generate, keep changes small enough to review, and manage context so the model stays grounded in your actual codebase.
Plan-then-implement: outline the approach before generating codeKeeping diffs small and reviewable instead of one giant generationManaging context: what to include (files, errors, specs) vs. what to omitIterating in a loop: generate, run, feed results backKnowing when to write it yourself vs. delegate to the agent
Prompting for Code
The quality of AI-generated code tracks the quality of the prompt: vague requests produce plausible-looking but wrong code, while specific, well-scoped prompts with the right context produce code you can actually trust.
Giving concrete acceptance criteria, not just a feature descriptionPointing to relevant files, types, and existing patterns instead of describing from scratchConstraining scope: one change at a time vs. open-ended refactorsAsking for a plan or tests first, then implementationIterating with corrective feedback instead of re-prompting from zero
Reviewing AI Output
AI-generated code fails silently more often than it fails loudly: it compiles, looks idiomatic, and cites APIs that don't exist, so reviewing it requires actively hunting for confident-sounding mistakes, not just skimming for syntax errors.
Spotting hallucinated APIs, packages, and function signaturesChecking that cited behavior matches the actual library docs/versionWatching for subtly wrong logic that still passes a casual readVerifying claims ('this handles edge cases') instead of trusting themDiffing against existing patterns to catch inconsistent conventions
Tools & Integration
Knowing the tool landscape — CLI agents, IDE assistants, and the protocols connecting them to your codebase — determines how much real leverage you get versus how much time you spend fighting the tool.
CLI/agentic coding tools (Claude Code) vs. IDE-embedded assistants (Copilot, Cursor)Model Context Protocol (MCP): giving an agent tools and live data sourcesProject-level config/instructions files (e.g. AGENTS.md, CLAUDE.md) that steer agent behaviorCustom instructions and rules for tailoring review/generation to a codebaseChoosing the right tool for the task: autocomplete vs. multi-file agentic changes
Testing & Verification
An AI agent's claim that code 'works' is not evidence — the only way to trust AI-assisted changes is to make the agent (or you) actually run tests, execute the code, and observe real output.
Running the test suite after every AI-generated change, not just reading the diffAsking the agent to write tests before or alongside implementationHaving the agent execute/verify its own work (run it, show output) instead of asserting successCatching regressions AI introduces in code it didn't touch directlyUsing linters, type checkers, and CI as an automated second reviewer
Pitfalls & Safety
AI coding tools introduce new failure modes on top of ordinary bugs: overreliance erodes your own understanding, untrusted content can inject hidden instructions, and generated code can leak secrets or ship exploitable flaws if nobody applies real security review.
Overreliance: staying able to read/debug code you didn't writePrompt injection via untrusted content (issues, web pages, docs the agent reads)Security-reviewing AI-written code the same as human-written codeSecrets and credentials leaking into prompts, logs, or generated filesExcessive agent permissions/autonomy on sensitive operations (deploys, deletes, prod data)
