knowledge-base

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Knowledge Base

A knowledge base is a self-service library of structured content that allows users to find answers without contacting support. Done well, it deflects tickets, reduces support cost, and builds user confidence. Done poorly, it becomes a graveyard of outdated articles that users stop trusting. This skill covers the full lifecycle: designing an information architecture that mirrors how users think, writing articles that scan instead of demand reading, optimizing search so the right article surfaces on the first try, measuring deflection to prove business value, and maintaining content ruthlessly so it stays accurate.

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When to use this skill

Trigger this skill when the user:

• Needs to design or restructure a help center or knowledge base taxonomy
• Wants to write, improve, or template a support article (how-to, troubleshooting, FAQ, reference)
• Is optimizing help center search - keywords, synonyms, metadata, or article titles
• Wants to measure deflection rate or build a content health dashboard
• Needs to implement in-product contextual help or tooltip copy
• Is building or refining an article maintenance workflow or content review cadence
• Wants to create article templates by type (how-to, troubleshooting, FAQ, reference)
• Needs to audit existing knowledge base content for gaps or staleness

Do NOT trigger this skill for:

• Writing internal engineering runbooks or operational playbooks (use incident-management skill)
• Writing API documentation or developer docs (use technical-writing or developer-experience skill)

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Key principles

1. Write for scanning, not reading - Users arrive with a specific problem and scan for the answer. Use short paragraphs, numbered steps, bold key terms, and clear headings. A wall of prose is an article no one reads. Every section should be findable with a 2-second scan.

2. Structure mirrors the user's mental model - Organize content around tasks users are trying to complete and problems they experience, not around your product's internal feature structure. "How do I invite a teammate?" beats "User Management > Invitations > Creating Invitations." Users think in outcomes, not menus.

3. Search is the primary navigation - Most users will never browse your category tree. They will type a query and click the first plausible result. Every article title, summary, and keyword set must be optimized for the words users actually type, not the words your product team uses internally.

4. Measure deflection, not pageviews - Pageviews tell you what people look at. Deflection tells you whether it worked. Track ticket volume versus help center traffic, article ratings, failed searches, and contact-us clicks post-article-view. A high-traffic article with a high contact-us rate is a failing article.

5. Maintain ruthlessly - An outdated article is worse than no article. It creates false confidence and support tickets filled with "I followed the article and it didn't work." Every article needs an owner, a review date, and a clear process for marking it outdated or archiving it when the feature changes.

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Core concepts

Information architecture

Information architecture (IA) is how content is organized, labeled, and linked. A good IA means users can find answers in two clicks or fewer from the help center home page.

Taxonomy design principles:

• Top-level categories map to user goals, not product features
• 5-8 top-level categories is the practical maximum before navigation becomes overwhelming
• Each article belongs to exactly one primary category (cross-links are fine; dual-homing creates maintenance debt)
• Category names use plain language: "Billing & Payments" beats "Revenue Operations"
• Sub-categories add one level of specificity - avoid nesting beyond two levels

Taxonomy validation test: Show the category structure to five users who have never seen it. Ask them where they would look for a specific common task. If fewer than four out of five find the right category, redesign the labels.

Article types

Type	Purpose	Primary user intent
How-to	Step-by-step instructions for a task	"I want to do X"
Troubleshooting	Diagnose and fix a specific error or symptom	"X is broken or not working"
FAQ	Short answers to common questions	"I have a quick question about X"
Reference	Complete spec, options table, or glossary	"I need to know all the values/settings for X"
Concept	Explains a feature or workflow at a high level	"I want to understand how X works before I use it"

Most articles should be how-to or troubleshooting. If your knowledge base is mostly concept articles, users are not finding actionable answers - they are being educated when they want to be unblocked.

Search optimization

Search in a knowledge base is keyword-matching plus ranking, not semantic understanding (even with AI-powered search, explicit optimization still wins).

The three-layer keyword strategy:

Layer 1 - Title keywords:  Words users type when they know what they want
                           ("reset password", "cancel subscription", "export CSV")

Layer 2 - Synonyms:        Alternate terms for the same concept
                           ("reset" = "forgot", "change", "recover")
                           ("cancel" = "delete account", "close account", "unsubscribe")

Layer 3 - Error strings:   Exact error messages users copy-paste into search
                           ("Error 403: Forbidden", "SMTP authentication failed")

Store synonyms in your search tool's synonym dictionary so both terms resolve to the same results. Never make users guess the "right" terminology.

Deflection metrics

Deflection is the percentage of users who find an answer in the knowledge base and do not open a support ticket. It is the primary health metric for a knowledge base.

Deflection rate formula:

Deflection rate = 1 - (tickets opened after KB visit / total KB visits)

Supporting metrics to track:

Metric	What it measures	Healthy target
Deflection rate	Overall KB effectiveness	> 70%
Article rating (thumbs)	Per-article satisfaction	> 80% positive
Failed search rate	Queries returning zero results	< 10%
Contact-us click rate post-article	Articles that fail to resolve	< 5% per article
Article staleness (days since reviewed)	Content freshness	< 180 days
Search-to-click rate	How often search results get clicked	> 60%

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Common tasks

Design help center architecture - taxonomy

Step 1: Mine your ticket data

Pull 90 days of support tickets and tag each with the user's underlying goal (not the feature involved). The top 10 goals by volume become your category candidates.

Step 2: Card-sort validation

Give 8-10 representative users 20-30 article titles on cards. Ask them to group articles into categories and name each group. Patterns appearing in 6+ of 8 users' groupings are validated categories.

Step 3: Build the hierarchy

Level 0: Help Center home
Level 1: 5-8 goal-based categories  (e.g., "Getting Started", "Billing", "Account Settings")
Level 2: Sub-categories per Level 1  (e.g., "Billing > Invoices", "Billing > Payment Methods")
Level 3: Individual articles

Step 4: Map existing content

Audit every existing article against the new taxonomy. For each article: keep, merge, rewrite, or archive. Do not migrate stale articles - migration is a forcing function to decide whether content is worth keeping.

Write effective support articles - template

See references/article-templates.md for full templates by article type.

Universal writing rules:

• Title format: verb + object + optional context. "Reset your password" not "Password Reset"
• First sentence: state exactly what the article covers and who it is for
• Steps use numbered lists; sub-steps use indented numbered lists
• Add screenshots and videos for steps where UI placement is ambiguous
• Bold the first mention of a UI element: "Click Save changes"
• End every article with a "Was this helpful?" rating and a link to contact support

Length targets:

Article type	Target word count
How-to	150-400 words
Troubleshooting	200-600 words
FAQ	50-150 words per answer
Reference	As long as needed; use anchor links for navigation

Optimize search - keywords and synonyms

Keyword audit workflow:

1. Export failed searches from the last 30 days (queries with zero results or zero clicks)
2. Cluster similar failed searches into synonym groups
3. For each cluster: does a relevant article exist? If yes, add synonyms. If no, add to content backlog.
4. Export low-click-rate searches (results shown but not clicked) - these indicate title mismatch
5. Rewrite article titles to match the language users use in low-click queries

Building the synonym dictionary:

Group: password
Synonyms: forgot password, reset password, change password, recover account,
          locked out, can't log in, login help

Group: cancel account
Synonyms: delete account, close account, unsubscribe, remove account,
          stop subscription, leave [product name]

Group: billing
Synonyms: invoice, receipt, charge, payment, credit card, subscription cost, price

Review and expand the synonym dictionary every quarter using fresh failed-search data.

Measure and improve deflection rate

Deflection measurement setup:

1. Instrument your help center with session tracking
2. Define a "deflection event": user visits KB and does NOT click "Contact Support" or open a ticket within the same session
3. Define a "failure event": user visits KB AND opens a ticket or clicks "Contact Support"
4. Calculate: deflection rate = deflection events / (deflection + failure e

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