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1 hour ago
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Claude Code is my daily companion for building. It scaffolds services, fills in boilerplate, and answers the “what’s the shape of this interface” questions before I’ve even opened the docs. But the way I really move fast is not by coding in the same session forever. It’s by cutting the cord. I call the pattern forced context distillation.
The Problem With Continuous Sessions
AI planning sessions get messy. You wander through alternatives, reject half of them, pile up tokens with half-baked thoughts, and let assumptions creep in that aren’t visible anymore. If you keep coding in that same thread, you drag all of that baggage forward. It slows you down and muddies the outcome.
That’s the failure mode: momentum built on stale context.
The Core Idea: Forced Context Distillation
Forced context distillation is the opposite. You don’t let the model keep running with everything it has “in its head.” You make it stop. You force it to write a precise, fully scoped implementation prompt that a completely separate agent can follow cold.
Fresh session. Clean state. Only the distilled prompt passes through.
That separation forces clarity. It turns wandering brainstorming into a reproducible spec. The prompt contains everything: file paths, TypeScript signatures, phases, deliverables, exit criteria, and definition of done all in one artifact that serves as both plan and implementation contract.
Why It Works
| Context | Bloated with old decisions | Tight and explicit |
| Drift | Hidden assumptions creep in | Everything written down |
| Reproducibility | Hard to replay | Easy to rerun from markdown |
| Onboarding | Needs the whole messy thread | New agent just reads the plan |
| Failure Mode | Half-remembered intent | Missed item is obvious |
The boundary is what makes it powerful. The planner explores, then hands off. The implementer executes.
The Workflow
flowchart LR A[Plan with Claude Code] --> B[Distilled Implementation Prompt] B --> C[New Session] C --> D[Phased Implementation] D --> E[Tests, Docs, Release]Step 1: Plan and Distill
In the planning session, I ask Claude Code for a complete implementation prompt. Not vague outlines, but specific file paths, TypeScript signatures, mermaid diagrams, test commands, risks, rollback steps, and a definition of done.
The prompt becomes a self-contained artifact that tells a clean new session how to work phases, deliverables, exit criteria, self-check rubrics, even what commit messages to use. Everything needed is in one place.
The prompt is saved in the repo. Then I close the planning session entirely.
Step 2: Implement Fresh
I open a new Claude Code session. The only context it gets is the implementation prompt. No baggage. No wandering assumptions.
The agent runs phase by phase, following the prompt’s guidance. Small diffs, frequent test runs, visible outputs. If it hits an external dependency, it stubs with a TODO and records the unblock steps. It keeps going until the definition of done is satisfied.
Step 3: Ship
By the time the final phase is complete, I have not just code but a living artifact: the prompt that guided the work, the changelog, and the recorded status. It’s an audit trail and an onboarding doc rolled into one.
An Example: Lot Lookup Endpoint
The implementation prompt defines contracts first:
// packages/contracts/src/lots.ts export interface LotLookupRequest { lotCode: string; } export interface LotLookupResponse { lotCode: string; productCode: string; status: "available" | "allocated" | "consumed"; quantity: number; uom: "kg" | "lb" | "ea"; }Then the prompt drives phases:
| 1 | Scaffold | Workspace, configs | Build passes |
| 2 | Contracts | DTOs and validators | Unit tests green |
| 3 | Handlers | Routes and services | Curl checks match |
| 4 | Tests & Docs | Integration tests, runbook | Integration tests green |
| 5 | Release | Changelog, notes | Definition of done passes |
Every step is measurable. Every failure is local and obvious.
The Payoff
The planner explores. The implementer executes. The cut between them forces everything into a distilled prompt that is small, sharp, and reusable.
This is what makes AI not just a coding helper, but a reproducible system for shipping features. It’s the fastest way I’ve found to move from idea to working code without dragging yesterday’s noise into today’s build.
