What documents does AI need to work?

AI needs: knowledge base documents (policies, FAQs, product info), process documentation (SOPs for tasks AI handles), data dictionaries (what fields mean), and decision criteria (how to make decisions). The better your documentation, the better AI performs. Undocumented processes = poor AI performance.

How do I create AI-ready documentation?

Make documentation: structured (clear sections, tables), explicit (state what's implied), versioned (track changes), searchable (tags, keywords), and comprehensive (cover edge cases). AI processes well-organized docs better than long unstructured paragraphs. Include examples of correct outputs.

What if my processes aren't documented?

Undocumented processes must be documented before AI implementation. This is actually an opportunity—it forces you to clarify processes. Greene Solutions can help with process documentation as part of implementation. Budget 2-4 weeks for documentation if you don't have existing SOPs.

What Documentation Do I Need for AI Implementation?

AI is only as smart as what it knows. The documentation you provide becomes its knowledge. Here's what to prepare.

Documentation Categories

Category	Purpose	Examples
Knowledge Base	Facts AI needs	Products, policies, FAQs
Process Docs	How to do things	SOPs, workflows
Data Dictionary	What data means	Field definitions
Decision Criteria	How to decide	Rules, policies
Operational	How to run AI	Runbooks, alerts

Knowledge Base Documents

The core information AI needs:

Product/service information: What you offer, pricing, features
Policies: Return policy, warranty, terms
FAQs: Common questions and answers
Technical specs: Details about products
Company info: About, contact methods, hours

Process Documentation (SOPs)

Step-by-step guides for tasks AI will handle:

Standard Operating Procedures: Clear steps, clear decisions
Decision trees: If X, then Y
Escalation criteria: When to involve humans
Templates: Standard responses, email formats
Examples: Sample inputs and correct outputs

Data Dictionary

Define what your data means:

Field definitions: What each database field contains
Value meanings: What codes, statuses, types mean
Relationships: How data connects
Valid values: What's allowed in each field

Decision Criteria

How AI should make decisions:

Approval rules: When to approve vs escalate
Priority levels: What's urgent vs routine
Routing rules: Where to send different requests
Threshold values: Numeric cutoffs for decisions

AI-Specific Formatting

Make documentation AI-friendly:

Structured format: Headers, bullets, tables
Explicit statements: Say what you mean directly
Consistent terminology: Same terms throughout
Examples included: Show correct outputs
Version control: Track document changes

Common Documentation Gaps

Gap	Impact	Fix
No SOPs	AI doesn't know process	Create before AI
Implicit rules	AI misses unwritten knowledge	Document explicitly
Outdated docs	AI gives wrong info	Regular reviews
No examples	AI format inconsistent	Add sample outputs

Documentation Timeline

How long documentation takes:

Existing docs: 1-2 weeks to organize
Partial docs: 2-4 weeks to complete
No docs: 4-8 weeks to create from scratch

Greene Services: Documentation Help

We help clients:

Audit existing documentation
Create process maps and SOPs
Structure docs for AI consumption
Set up version control and updates

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