Planning Major Features with AI: A Practitioner's Guide¶

Planning significant features that span backend APIs, data models, UI, and infrastructure remains one of the most demanding aspects of software engineering. The complexity lies not just in execution, but in the rigorous architectural thinking required to anticipate dependencies, failure modes, and long-term technical debt.

As engineering teams mature in their adoption of AI, the focus is shifting beyond simple code generation toward higher-level architectural assistance. The challenge is no longer just writing functions faster, but leveraging AI to decompose complex requirements into manageable work streams, rigorous technical specifications, and clear implementation strategies.

This article outlines a practical workflow for integrating AI into technical planning, focusing on how the right process can help teams stress-test designs and build more robust implementation plans.

Where AI helps (and where it doesn't)¶

AI is at its best when the work is about breadth and structure:

• Mapping the codebase quickly when you point it at real files
• Listing realistic design options and trade-offs
• Pulling constraints from platform documentation
• Turning discussions into a plan document you can share and diff

It is not a substitute for product judgment, domain nuance, or accountability. Ultimately, the engineering owner remains responsible for the risk profile and technical viability of the plan.

A workflow beyond simple generation¶

A common pitfall is requesting a single comprehensive plan and implementing it without scrutiny. A more effective approach treats the assistant as a participant in a design review: proceeding iteratively and maintaining synchronization regarding the plan document.

1. Frame requirements with explicit constraints¶

Begin with a concise brief that concretizes the problem space. An effective prompt typically includes:

• What you are building and why it matters
• Success criteria (latency targets, adoption goals, support burden you want to reduce)
• Hard constraints (platform limits, backward compatibility, compliance rules)
• Explicit scope boundaries (what is in, what is out)
• Pointers to the real system (files, interfaces, patterns, existing similar features)

If you can, attach the source files instead of describing them. The plan quality jumps when the model can verify assumptions against types, APIs, and tests.

Here is a template that works well:

We want to add a database-backed cache for order lookups.

Constraints: 1 GB storage cap, must preserve existing API behavior, p95 latency under 200 ms.

Scope: cache lookup + invalidation, no UI redesign.

Context: here are the current order service, provider interface, and the UI component that reads from it.

Please propose a step-by-step implementation plan with milestones, key decisions, and open questions.

If the feature depends on an external platform or service, have the assistant read the relevant docs early. Discovering a hard limit after you have socialized the plan is expensive. Discovering it during the first draft is basically free.

2. Generate a first draft plan you are willing to critique¶

Ask for a plan that is structured enough to review. In practice, that means:

• Milestones with deliverables you can ship
• Dependencies between steps
• A short list of decisions that affect the design
• A list of open questions and risks

Concretely, you want a single plan artifact with milestones, a short decision log, and an open-questions checklist that you can keep updating as the conversation evolves.

The first draft serves as a straw man. Inaccuracies are acceptable at this stage, as the primary goal is to provide the team with a tangible artifact to evaluate.

A high-leverage strategy is to instruct the assistant to generate the plan directly into a repository file (e.g., docs/feature-plans/<feature>.md). This transforms the plan into a living document subject to standard code review practices.

3. Review design decisions one at a time¶

Most planning failures come from unexamined defaults. The assistant can help by turning an implicit choice into an explicit decision.

For each decision, ask for:

1. Why the decision matters downstream
2. A small set of realistic alternatives
3. Trade-offs (performance, complexity, operability, portability)
4. A recommendation and the assumptions behind it

Then pressure test with your real constraints. When the assistant says something is "negligible", change the numbers.

Here is the interaction pattern that keeps conversations productive:

Assistant: Decision 1 - cursor-based pagination vs offset-based. Here are options and trade-offs.

You: Accept cursor-based. Next decision.

Assistant: Decision 2 - store IDs as UUIDs for index efficiency.

You: Our IDs are external strings from third-party systems. Re-evaluate.

As decisions get locked in, update the plan document immediately. Otherwise the real reasoning ends up trapped in chat history.

4. Turn "open questions" into a checklist with owners¶

Every real plan has unknowns. The goal is not to eliminate them all up front - it is to make them explicit, prioritize them, and avoid surprises that force rework.

A simple categorization works well:

• Blocking: must be answered before implementation starts
• Step-blocking: must be answered before a specific milestone
• Deferrable: can be answered during implementation without changing architecture

This is also where product and engineering planning meets. Some questions are technical research. Others require human decisions.

Examples that usually need product input:

• How fresh does the data need to be
• What failure mode is acceptable (stale data vs an error)
• Who gets paged when it breaks

Examples that usually need technical validation:

• Does the platform support the index / query pattern you need
• Can your existing data model represent the new state without breaking migrations
• What the worst-case performance looks like at real volumes

5. Iterate without losing coherence¶

As the plan evolves, the assistant should update the same document in-place rather than regenerating everything. This is where it can add real value as a "consistency checker".

Prompts that help:

• Review the current plan and list sections that are inconsistent with the latest decisions.
• Propose the minimal set of edits to bring the doc back into sync.
• Summarize the rationale for changed decisions in an appendix.

If you commit the plan as it changes, the git history becomes a timeline of how the design evolved and why.

Common Planning Anti-patterns¶

Accepting the first draft as definitive¶

Unchallenged assumptions will be integrated into the final plan. Treat the initial draft solely as a starting point for rigorous review.

Delegating product decisions to the model¶

Models can outline options, but they lack context regarding customers, roadmap pressures, and support constraints. Leverage them for analysis, but retain decision-making authority.

Debating in chat without documentation updates¶

The successful loop is discussion, decision, and documentation. Omitting the documentation update sacrifices the value of the rigorous planning process.

Attempting to answer every uncertainty before coding¶

Some questions only yield to a technical spike or benchmark. A robust plan should include these experiments as early milestones.

Excessive planning iterations¶

If the assistant generates an exhaustive list of decisions, it is crucial to pace the review. Conclude the planning phase when remaining questions are low-impact and proceed to implementation.

Takeaways¶

AI-assisted planning works best when you treat it as an iterative review process:

• Provide real context (files, constraints, and scope boundaries)
• Generate a draft you can critique
• Walk decisions one-by-one and pressure test assumptions
• Track open questions with owners and deadlines
• Keep a single plan doc updated as decisions change

You end up with a plan that is clearer, easier to socialize, and less likely to collapse the moment implementation starts.