Why Your AI Coding Sessions Keep Drifting: The Planning Workflow That Stops It

Introduction

There is a moment every development team hits when they start using an AI coding assistant seriously.

You have a real feature: not a tutorial, not a proof of concept, but something with actual business logic, multiple moving parts, and a codebase that has been alive for months. Someone opens a new conversation, types "build me the quote submission flow," and watches the AI confidently produce something that looks reasonable but is subtly, expensively wrong. It used the wrong data shape. It ignored the existing pattern the team settled on six months ago. It built a new abstraction when a hook already existed for exactly this.

You fix it. You reprompt. The AI course-corrects. Then in the next session it drifts again.

This is not a flaw in the AI. It is a flaw in the workflow.

Over an extended period of building a production multi-portal platform with three portals, more than 80 documented feature implementations, a major monorepo refactor, and a production deployment on AWS, with Claude Code as our daily coding assistant, we found one workflow change that made a bigger difference than any other: planning the feature with the AI before asking it to build anything.

This article is about that workflow, why it works, and how to set it up for your own team. By the end, you will have a concrete process you can run on your next feature, one that makes AI output more consistent, reduces rework, and keeps the codebase coherent session after session.

The Problem with

The AI coding tools space has grown fast. 84% of developers are now either actively using or planning to adopt AI coding tools, and GitHub reports that over 51% of all code committed to its platform is generated or substantially assisted by AI. The speed gains are real.

But there is a gap the adoption numbers do not show: usage and trust are moving in opposite directions. More developers use these tools, fewer say they fully trust the output. That gap is not a model quality problem. It is a workflow problem.

Here is the core issue. Every new conversation with an AI assistant starts cold. It has no memory of the architectural decisions made three months ago, the data-fetching pattern the team standardized, or the reason the delete confirmation dialog works a specific way. On a simple task this barely matters. On a complex feature with ten interconnected components, it compounds into inconsistency, rework, and sessions that feel like they are fighting the tool rather than using it.

On our platform, the scale made this risk obvious from the start. The codebase spans three portals: an admin portal, a customer portal, and a partner portal, with a rich shared domain spanning products, customers, transactions, and reporting. Features are not isolated. Every new piece touches existing patterns. We could see that context drift would be expensive here, so rather than discover that mid-feature, we built the planning workflow from day one.

The answer was not longer prompts. It was to invest time upfront in a structured plan before a single line of implementation code was written.

Better context leads to better AI-generated software

Why Planning-First Changes Everything

The clearest illustration came when implementing a dynamic multi-step data entry form that served as a core workflow for the platform. Each record type had its own set of configurable fields. The form needed to adapt at runtime based on user selection, validate dynamically, and submit to a backend processing API.

The naive approach would have been to describe the feature and ask Claude to build it. Instead, we opened a planning session first. No code was produced in that session. What came out was a set of documents that mapped directly to six steps: a staircase of decisions that took the feature from a vague requirement all the way to a testable, shippable piece of software.

Here is what that staircase looked like in practice, using the quote form as a real example:

Step 1: Define Scope

Before writing anything, the planning session forced a clear answer to: what does this form need to do, and what is explicitly out of scope for this version? The form would support multiple products with configurable fields. It would not handle payment processing or final submission, which were separate features. That boundary, agreed upfront, prevented scope creep from appearing mid-implementation.

Step 2: Break Down Components

The form was not one thing. It was a multi-step flow, a validation layer, a product configuration loader, a calculation API integration, and a submission handler. The planning session broke these into discrete pieces and established the order in which they needed to be built. Each piece had a clear input and output. Claude could be given one piece at a time rather than being asked to hold the entire feature in mind at once.

Step 3: Make Technical Choices

This is where the Architecture Decision Record came in. For each significant choice (how the form would adapt to different products, how field-level performance would be managed, how the data would be structured before submission), the team documented the option chosen and the reason. Not every choice needs recording. The ones that could have gone multiple ways, and where the wrong choice would be expensive to undo, do. Each entry took roughly fifteen minutes to write and prevented entire categories of rework in later sessions.

Step 4: Create a Checklist

Once scope and components were clear, the planning session produced a step-by-step implementation checklist. This became the anchor for every subsequent coding session. Instead of starting each session by re-explaining where the feature stood, the team opened the checklist, marked what was done, and handed Claude the next unchecked item with the relevant context. Progress was visible and sessions stayed focused.

Step 5: Implement the Feature

With the plan in place, implementation sessions were faster and more consistent than any comparable feature built without one. Claude was not being asked to make structural decisions mid-build. Those were already made and recorded. It was being asked to execute against a clear spec, which is what it does best.

Step 6: Test Thoroughly

Testing strategy was also decided during planning, not after the fact. An earlier approach had produced 2,984 lines of tests that broke whenever the internal structure of a component changed, even when the behavior seen by the user had not changed at all. The planning session made a deliberate decision to test user behavior instead of internal implementation. The result was 1,998 lines of tests: 33% fewer, and resilient to refactoring.

The six steps together took about an hour in the planning session. The implementation that followed was faster, more consistent, and required significantly less rework than any feature of comparable complexity built without a plan.

The six-step planning workflow that transforms feature ideas into predictable, testable implementations.

The Workflow: Four Phases Before Any Code

Here is the planning-first workflow as we now run it on every non-trivial feature.

Before You Start: Assemble the Context Kit

A planning session is only as good as what you bring to it. Walking in with a vague feature description and asking Claude to produce a plan will generate something generic: technically plausible, but not aligned to your project. Before opening the session, prepare three things:

1. Your project's implementation guide
   This is a document that captures your team's coding standards, architectural patterns, and conventions. On our project, this included things like which folder structure to follow, how hooks and services are organized, which UI component patterns to use, and how state management is handled. When this guide is attached to a planning session, Claude extracts the relevant rules from it and bakes them directly into the plan it produces. The output is not just a plan. It is a plan that already speaks your codebase's language.

2. The full feature requirements
   Not a one-liner. A proper description of what you want to build: the user-facing behavior, the edge cases, any constraints, the role-based access rules if applicable, and how it connects to existing features. The more precise this is, the less the planning session drifts into assumptions.

3. The API specification
   Every feature that touches a backend endpoint should go into a planning session with the full API spec attached: the endpoint paths, the request shape, the response shape, the error cases. This grounds the plan in what the backend actually provides rather than what the team hopes it provides. It also surfaces mismatches between what the frontend needs and what the API delivers, before a line of code is written, not after.

These three inputs together transform the planning session from a brainstorm into a precise, project-aware blueprint. Claude is no longer guessing at your patterns or your data shape. It has the information it needs to produce a plan that could go directly into implementation.

Phase 1: Define Goal and Constraints Precisely

With the context kit in hand, open the planning session. The first message is not "build X." It is:

"We need to implement [feature]. Here is our implementation guide, the feature requirements, and the API spec. Help us think through the full scope, the unknowns, and the approach before we write anything."

Treat the AI as a thinking partner at this stage. Ask it to surface what the team is not thinking about. Ask it to identify dependencies. Ask it to flag where the API spec leaves something uncertain that needs clarification before implementation begins.

This phase reliably surfaces two or three hidden complexities that would otherwise be discovered mid-implementation, the kind that cause features to balloon in scope when the team is already halfway through.

Phase 2: Build an Implementation Plan

Ask the AI to produce a structured implementation plan. The document should include:

• Goal statement: what the feature does and why it matters to users

• Out of scope: explicitly what is not being built in this iteration

• Component breakdown: what needs to be built, in what order, and why that order

• API assumptions: what the backend is expected to provide; flag anything uncertain

• Design decisions: choices being made that could go multiple ways; record which one is taken and why

• Checklist: a step-by-step list of implementation tasks that can be checked off session by session

For the quote form, this plan covered the full feature end to end. For the monorepo refactor, which involved extracting 10,000-plus lines of duplicated code across three portals into two shared packages, the plan document reached 833 lines before any code was touched. That investment paid back across every subsequent session.

The checklist is not just for you. It is for the next session's version of the AI, which will have forgotten everything.

Phase 3: Write an Architecture Decision Record for Non-Obvious Choices

An ADR is a short document recording: the context, the decision, the alternatives considered, and the consequences. Not every feature needs one. Any feature that involves a significant architectural choice does.

The format is deliberately lightweight: a few paragraphs per decision. The goal is not ceremony. It is a searchable record that future sessions can reference instead of re-deriving the same conclusion from scratch.

Phase 4: Use the Plan as a Session Anchor

Once the plan exists, every implementation session starts the same way: feed the AI the full plan. It reads the checklist, sees what is already done, and picks up from where the previous session left off, with no re-explanation needed.

This is the step most teams skip. It is also the one that matters most. Without a session anchor, each conversation drifts. With it, the AI picks up context immediately and produces output that is on-pattern for the codebase rather than generic.

How structured planning transforms feature development from uncertainty into execution.

What Happens Without a Plan

Without this discipline, sessions drift. The AI has no memory of the decisions made an hour ago in a different conversation. It will re-derive conclusions, make inconsistent choices, and miss steps that were implicit rather than written down. The more complex the feature, the more expensive that drift becomes, because each inconsistency compounds the ones before it.

Picture a session where the AI produces a component that works perfectly in isolation, clean code with no errors, but using a different state management pattern than the rest of the feature, because that pattern was not in the prompt. Now there are two approaches coexisting in the same codebase. The next session inherits both, picks one arbitrarily, and the inconsistency spreads. No single session was wrong. The accumulation was.

The AI cannot account for what it was not given. Planning is how you give it what it needs.

The Benefit That Has Nothing to Do with AI

There is something that happens when a team writes a proper implementation plan that is entirely independent of the AI.

Decisions that were previously being made unconsciously get articulated. Questions that were not known to exist get surfaced. Scope creep becomes visible before it costs a sprint. And the record of why a feature was built a certain way is preserved, which is invaluable three months later when someone asks, or when the feature needs to be extended.

Addy Osmani, writing about his LLM coding workflow, puts it this way: the difference between high-output engineers and everyone else is the structure they build around the AI before execution starts. Planning collapses ambiguous decisions into a reviewed spec. Without it, every decision point during implementation is an improvisation.

The planning phase is good engineering practice that AI makes even more valuable, not the overhead that AI imposes on the team.

How to Set This Up on Your Own Project

No sophisticated system is required. Here is the minimal setup:

1. Create a dedicated folder in your repository: `claude-docs/`, `ai-docs/`, `planning/`, or any name that works for your team.
   This is where implementation plans, ADRs, and progress notes live. The exact name doesn't matter. What matters is keeping them in one place, version-controlled alongside the code, so they evolve with the codebase and are available to every team member.

2. Name files predictably
   A format like `FEATURE_NAME_IMPLEMENTATION_PLAN.md` and `ADR-NNN-DECISION-TOPIC.md` makes them easy to find and reference in prompts. Over time, this folder becomes the institutional memory of every significant technical choice made during the build.

3. Start every non-trivial feature with a planning session, not a coding session
   The prompt: "Help us plan [feature]. We need an implementation plan and a list of architectural decisions to record before we start."

4. Feed the full plan at the start of every implementation session
   The AI reads the checklist, determines what has already been done, and decides where to continue, with no manual re-orientation needed.

5. Update the plan as the feature evolves
   If something discovered during implementation contradicts a plan assumption, update the plan. These documents are living references, not fixed specs.

What You Will Notice After a Few Features

The first time this workflow is run end to end, the planning session will feel slow. The temptation to skip it and start building is real.

Resist it.

After three features using this approach, the pattern becomes clear: implementation sessions are faster, the AI's output is more consistent, complexity is caught before it becomes expensive, and the codebase has attached documentation for decisions that would otherwise become lost tribal knowledge.

The AI has not changed. The workflow around it has. And that, more than any model update or new tool feature, determines whether AI-assisted development consistently compounds a team's output or just adds a new source of noise to fix.

The Bottom Line

Planning first is not overhead. It is the leverage point.

It is how a team turns an AI that starts every conversation cold into a reliable coding assistant that builds on prior decisions, respects the established architecture, and delivers consistent output session after session.

One plan. Thirty to sixty minutes. Before the first line of code.

That discipline, held consistently across 80-plus features, is the single practice we would repeat without hesitation on any future project. Not the specific tools. Not the exact prompts. The discipline of planning before building.

That is the workflow.