Context Buckets: The Complete Guide

Why Context Engineering Matters

What is a Context Bucket?

A Context Bucket is a container for related knowledge that gets injected into your AI conversations. Think of it as a folder of documents that your AI can read before answering any question.

Context Bucket = Project Container + Goals + Accumulated Context

Where:
├── Project Container: Business domain (Marketing, Sales, Finance, etc.)
├── Goals: Grit Monitors defining success criteria
└── Accumulated Context: Documents, patterns, and history

Example: Multi-Domain Context Architecture

Company/
├── Marketing/
│   ├── brand-guidelines.md (5,200 tokens)
│   ├── campaign-templates.md (3,100 tokens)
│   └── competitor-analysis.md (8,400 tokens)
├── Sales/
│   ├── pricing-model.md (2,800 tokens)
│   ├── objection-handling.md (4,200 tokens)
│   └── pipeline-stages.md (1,900 tokens)
├── Operations/
│   ├── architecture.md (12,000 tokens)
│   ├── deployment-sops.md (4,800 tokens)
│   └── incident-playbook.md (3,600 tokens)
└── Legal/
    ├── terms-of-service.md (8,900 tokens)
    └── privacy-policy.md (7,200 tokens)

Bucket Categories for Every Business Function

GritFlow isn't just for code. Organize context for every part of your business.

Token Economics: How Much Context?

Tokens are the currency of AI context. More tokens = more context = smarter responses. But there's a balance—too much context can slow things down or exceed model limits.

Grit Monitors: Goals That Persist

Grit Monitors are long-term goals that persist across 100+ prompts. Unlike one-shot instructions, monitors ensure consistency in everything your AI does.