AI agents are getting smarter every month — but in production, accuracy still breaks for the same reason: knowledge.
When customers ask about pricing, policy exceptions, delivery windows, troubleshooting steps, or refunds, your assistant can’t “guess.” It needs a reliable source of truth, clear retrieval, and rules for what to do when information is missing.
Monobot includes a built-in Knowledge Base designed to organize information into categories, improve retrieval with keywords, and keep content editable over time. This article is a practical, step-by-step playbook to build a KB that stays accurate in real conversations — voice or chat.
Why Knowledge Bases fail (and what “good” looks like)
A Knowledge Base fails when it is:
- Too broad (one giant document → weak retrieval)
- Outdated (policies change, KB doesn’t)
- Written like internal docs (hard to answer from, full of context but few conclusions)
- Not measurable (no feedback loop, no QA)
A good KB is:
- Structured (categories mirror real user intents)
- Searchable (keywords/titles reflect how customers ask questions)
- Actionable (answers include steps, constraints, and next actions)
- Maintained (updates + logging + review process)
- Measured (you can see what breaks and fix it)
Step 1) Start with a “Top Questions Inventory” (before writing anything)
Pull 30–100 real questions from:
- call transcripts / chat logs
- support tickets
- FAQ pages
- internal SOPs (only as source material)
Then cluster into intents like:
- Pricing & plans
- Refunds & cancellations
- Shipping / delivery / scheduling
- Account & billing
- Troubleshooting
- Compliance / identity verification
- Escalation & human handoff
This becomes your category map.
Step 2) Build Knowledge Categories that match customer intent
In Monobot, the Knowledge Base is organized into categories, and you can upload/manage text documents and keep them grouped for better retrieval.
A practical starter structure:
- Product & Plans
- Billing & Payments
- Policies (Refunds, Terms, SLA)
- Setup / Onboarding
- Troubleshooting (by symptom)
- Integrations & APIs (if relevant)
- Escalation Rules (when to hand off)
Tip: if a category grows too_expand it_: split by intent (“Billing” → “Invoices”, “Failed payments”, “Plan change”).
Step 3) Write KB entries in “Answer-First” format (not like internal docs)
The #1 upgrade you can make: write the answer customers need first, then supporting details.
Use this template per entry:
Title: Short, customer-style
Answer (2–5 lines): The direct resolution
Steps: Numbered instructions
Constraints / exceptions: Clear bullets
Escalation: When to transfer to human
Example (snippet format):
Title: “How do I change my billing email?”
Answer: You can update your billing email in Account → Billing Settings.
Steps: 1) Open… 2) Click… 3) Save…
Constraints: If invoice already issued…
Escalation: If you can’t access the account, contact support.
Step 4) Add Keywords like your customers speak
Monobot supports keywords and titles to enhance knowledge retrieval and navigation.
For each entry, add:
- synonyms (“refund” / “money back” / “chargeback”)
- common misspellings (if frequent)
- “how do I…”, “where can I…”, “I can’t…”
This is especially important for voice where users speak naturally and messily.
Step 5) Build guardrails: what the agent should do when KB is missing
Accuracy isn’t just about having an answer — it’s also about refusing to invent one.
Add a short “Policy: uncertainty” section inside your KB or system rules:
- If the KB doesn’t contain the answer → ask a clarifying question
- If the question affects money/legal/security → offer human handoff
- If the customer is angry/urgent → escalate faster
Monobot also supports workflows (Flows) and real-time escalation patterns in its platform content, so you can design consistent outcomes rather than improvisation.
Step 6) Keep the KB fresh with logging and a review loo
A KB isn’t “done.” It’s a living product.
6.1 Log what users actually ask
A simple win: store recurring unknown questions, edge cases, or requests into a structured log.
Monobot provides an action to append structured rows into a CSV linked to a Knowledge Base category — useful for logging tickets, orders, or feedback.
Example logging fields:
- date
- channel (voice/chat)
- intent
- question
- did KB answer? (Y/N)
- escalation? (Y/N)
- fix required (new entry / update / workflow)
6.2 Review weekly
Each week:
- Add missing entries
- Rewrite unclear answers
- Merge duplicates
- Update policy changes
Step 7) Measure the impact (and prove ROI)
Monobot has a real-time analytics feature set to monitor performance and compare interactions across voice and chat.
Track these KB-driven metrics:
- Containment rate (resolved without human)
- Escalation reasons (missing KB vs customer request)
- Repeat question rate (KB unclear)
- AHT change (time-to-resolution)
- Top failing intents (where to invest next)
Quick checklist (copy into your internal doc)
- List top 50 questions → cluster into 6–10 intents
- Create KB categories per intent
- Write answer-first entries + steps + exceptions
- Add keywords/synonyms per entry
- Define “uncertainty rules” + escalation triggers
- Log unknown questions into KB CSV
- Review weekly + track improvements in analytics
Final thought
The fastest way to improve an AI agent isn’t swapping models — it’s building a knowledge layer that’s structured, retrievable, and continuously maintained.
If you’re building with Monobot, start small: 6 categories, 50 entries, one logging table — and iterate weekly. Your accuracy (and customer trust) will climb immediately.
Want to see how Monobot handles knowledge + workflows in practice? Explore the platform and book a demo to map it to your use case.