Daily Workflows with AI

Integrating AI tools into your development workflow is not about replacing your engineering process -- it is about augmenting each phase with intelligent assistance while maintaining the rigor defined in Pillar 1: Engineering Discipline. This section provides a structured approach to daily workflows that maximizes AI leverage while preserving code quality and security.

A Typical Day: AI-Augmented Developer Timeline

The following timeline illustrates how an experienced developer integrates AI assistance throughout a standard workday. Adapt the timing to your schedule, but preserve the sequence and the quality checkpoints.

Time	Phase	Activity	AI Role	Human Role
9:00 - 9:30	Morning Planning	Review tickets, plan approach, set context	Summarize ticket context, suggest implementation approaches	Select approach, identify risks, define success criteria
9:30 - 10:00	Context Loading	Open codebase, review recent changes, load AI context	Analyze recent diffs, map dependencies	Verify AI understanding, correct misconceptions
10:00 - 12:00	Deep Coding Session 1	Implement features, write tests	Generate code scaffolds, suggest implementations, write tests	Review every suggestion, verify logic, ensure security
12:00 - 12:30	Mid-Day Review	Self-review morning code, run tests	Identify potential issues in written code	Judge quality, fix issues, decide what stays
13:30 - 15:00	Deep Coding Session 2	Continue implementation, refactor, debug	Assist with debugging, suggest refactors	Maintain architectural consistency, validate changes
15:00 - 16:00	Review Cycle	Review PRs from teammates	Summarize PR changes, flag potential issues	Apply judgment, verify AI flags, provide human feedback
16:00 - 16:30	Documentation	Update docs, write commit messages, log decisions	Draft documentation, generate summaries	Verify accuracy, add context AI cannot know
16:30 - 17:00	End-of-Day Wrap	Push code, update tickets, plan tomorrow	Generate status updates, suggest next steps	Confirm accuracy, prioritize next day

Phase 1: Morning Planning with AI

Begin each day by establishing context -- both for yourself and for your AI tools.

Step 1: Ticket Review. Open your sprint board and review your assigned stories. For each ticket, use AI to quickly summarize the requirements and identify related code areas.

Step 2: Approach Design. Before writing any code, describe the implementation approach to your AI tool and ask it to identify potential issues. This is not about getting the AI to design your system -- it is about using it as a sounding board.

Step 3: Risk Identification. Ask the AI to enumerate edge cases, potential failure modes, and areas where the implementation might interact with existing code in unexpected ways. Cross-reference with PRD-STD-007 risk categories.

tip

Spending 15 minutes on AI-assisted planning can save hours of rework. The most common mistake developers make is jumping straight into code generation without establishing context.

Phase 2: Context Loading

AI tools perform dramatically better when they have rich context. Invest time loading context before you start coding.

Effective context loading includes:

• Opening relevant files in your IDE so the AI can reference them
• Providing architectural constraints ("we use repository pattern," "this service must be stateless")
• Sharing coding standards ("follow the existing naming convention in this module")
• Referencing related tickets or past implementations ("similar to how we handled X in PR #1234")

Common context loading mistakes:

• Assuming the AI remembers yesterday's session (it usually does not)
• Providing too little context, resulting in generic or incorrect suggestions
• Providing too much context, overwhelming the model and degrading output quality
• Failing to correct AI misunderstandings before they propagate into code

Phase 3: Deep Coding Sessions

This is where AI provides the most visible productivity gains -- and where the greatest risks emerge. Structure your coding sessions with deliberate quality checkpoints.

The Generate-Review-Commit Cycle

Never commit AI-generated code without completing a full review cycle. Follow this pattern:

1. Prompt. Write a clear, specific prompt for the code you need (see Prompt Engineering)
2. Generate. Let the AI produce a candidate implementation
3. Read. Read every line of the generated code as if you were reviewing a PR from a junior developer
4. Verify. Check for correctness, security issues (see Security Awareness), and adherence to team conventions
5. Test. Run existing tests and write new ones for the generated code
6. Refine. Iterate on the prompt or manually edit the code until it meets your standards
7. Commit. Only commit code you fully understand and can defend in a code review

warning

The 1.7x issue rate in AI-generated code is largely attributable to developers skipping steps 3-6. Resist the temptation to accept AI suggestions without thorough review, especially under time pressure. See PRD-STD-002.

When AI Excels During Coding Sessions

• Boilerplate and scaffolding. CRUD operations, data models, API route handlers, configuration files
• Test generation. Unit test scaffolds, edge case enumeration, mock setup
• Pattern application. Applying known patterns to new contexts (e.g., "create a repository class like UserRepository but for Orders")
• Documentation. Docstrings, inline comments, README sections

When to Override AI and Code Manually

• Complex business logic. Multi-step workflows with nuanced rules that require deep domain understanding
• Security-critical code. Authentication flows, encryption, access control (always verify AI suggestions against PRD-STD-005)
• Performance-critical paths. Hot loops, query optimization, memory management
• Novel algorithms. Anything without well-established patterns in training data

Phase 4: Review Cycle

Dedicate at least one hour daily to reviewing code -- both your own AI-assisted code and your teammates' PRs.

Self-review checklist for AI-assisted code:

• I can explain every line of this code to a teammate
• I have verified the logic against the ticket requirements
• I have checked for hardcoded values, magic numbers, and assumptions
• I have run the full test suite and all tests pass
• I have checked for security issues per PRD-STD-005
• The code follows our team's style guide and architectural patterns
• I have not leaked any sensitive data into AI prompts

When reviewing teammates' AI-assisted PRs, apply the full review criteria from Code Review Responsibilities.

Phase 5: Documentation

AI can dramatically accelerate documentation, but accuracy verification is critical.

Use AI for:

• Generating initial drafts of docstrings and API documentation
• Summarizing complex code changes for commit messages
• Creating changelog entries from a set of commits
• Drafting architecture decision records (ADRs)

Always verify:

• Technical accuracy of AI-generated documentation
• Completeness -- AI often misses edge cases and limitations
• Tone and audience appropriateness
• References to other documentation or standards

Anti-Patterns to Avoid

Anti-Pattern	Why It Is Dangerous	What to Do Instead
Copy-paste coding	Accepting AI output without reading it	Read every line, understand every decision
Context amnesia	Starting each AI session from scratch	Maintain a context document or use persistent session features
Over-reliance	Using AI for tasks you should deeply understand	Build skills manually first, then use AI to accelerate
Prompt laziness	Writing vague prompts and accepting vague code	Invest in precise, context-rich prompts (see Prompt Engineering)
Review fatigue	Rubber-stamping AI code after a long day	Take breaks, use checklists, pair-review critical sections

Measuring Your Workflow Effectiveness

Track these personal metrics weekly to calibrate your AI-assisted workflow:

• AI acceptance rate: What percentage of AI suggestions do you accept vs. reject? A healthy range is 40-70%. Below 40% suggests poor prompting; above 70% suggests insufficient review.
• Rework rate: How often does AI-assisted code require changes after PR review? Target under 15%.
• Time-to-first-commit: Has AI reduced the time from ticket start to first meaningful commit? Track the trend over time.
• Defect introduction rate: Are you introducing more or fewer bugs than before AI adoption? This should decrease over time as your AI collaboration skills improve.

Share these metrics with your Development Manager during one-on-ones to support the team-level metrics described in Metrics That Matter.