The “Too Smart” Knowledge Base Problem: When Your AI Knows Too Much for Its Own Good
Source: Dev.to
I messed up. Not in a small way. In a “the client called me at 11 PM on a Friday” kind of way.
We had just deployed a healthcare clinic’s appointment‑booking system: Voice AI, fancy setup. Patients call, the AI schedules appointments, and that’s it. Or at least that was the plan.
The client handed us everything—15 years of medical documentation, treatment protocols, drug‑interaction guides, medical‑terminology databases, insurance policy documents, and more than 10 000 FAQ entries.
My brilliant idea was simple: feed it everything. More context equals smarter AI, right?
Wrong. Very wrong.
What Actually Happened
| Day | Call Summary |
|---|---|
| Day 1 | A patient called for an appointment for her daughter’s fever. The AI responded by explaining pediatric fever‑management protocols, age‑based criteria, and medical guidelines. The patient hung up. |
| Day 2 | Another patient asked for an appointment next Tuesday. The AI launched into copays, preventive‑care codes, insurance clauses, and coverage rules. The patient snapped, “I just want an appointment.” |
| Day 5 | The client called: the AI was giving medical lectures instead of booking appointments. Call volume was down 40 % in a week. |
That was the moment I realized what I had done.
The Problem I Created
The AI had become a know‑it‑all.
- I gave it access to medical knowledge bases because I feared patients would ask medical questions during booking.
- I didn’t consider the simple truth: just because you know something doesn’t mean you should say it.
The AI behaved like that party guest who turns every casual comment into a TED Talk. Someone says, “It’s hot outside,” and they launch into climate data from the past fifty years. You don’t argue—you just walk away. That’s exactly what patients were doing.
The Real Issue: Context Confusion
When a patient mentioned “fever,” the AI queried the knowledge base and retrieved hundreds of documents—protocols, drug interactions, emergency criteria, insurance rules. It assumed all of this was helpful and tried to share it.
The patient wasn’t looking for medical education; they were looking for a 3 PM slot on Tuesday.
The AI couldn’t distinguish between information it needed to schedule an appointment and information it merely had access to.
My First Failed Fix
I tried restricting it:
“Only provide medical information if explicitly asked.”
That didn’t work. A patient mentioned a cough and asked to see a doctor. The AI replied, “I won’t explain cough protocols unless asked,” and then immediately offered to explain them anyway. It became the awkward person who announces they’re not going to talk about something while talking about it.
Patients still hung up.
The Actual Solution: Role‑Based Knowledge Filtering
I rebuilt the entire prompt around one simple idea:
You are a receptionist, not a doctor.
Instead of limiting knowledge, I defined identity.
- The AI became Sarah, the friendly receptionist.
- Her only job: schedule appointments—warm, efficient, human.
- She had access to medical knowledge but was instructed to ignore it unless absolutely necessary for scheduling.
- She was not a medical advisor, diagnosis tool, insurance expert, or encyclopedia.
How Knowledge Was Actually Allowed to Be Used
| Allowed Use | Example |
|---|---|
| Match doctor specialties | “You need a pediatrician.” |
| Assess urgency | “Your symptoms suggest we should see you today.” |
| Answer basic logistics (e.g., fasting requirements) | “Please fast for 8 hours before the blood test.” |
Never used to:
- Explain conditions
- Discuss treatments
- Interpret symptoms
- Dive into insurance details
If a patient asked a medical question, the AI redirected politely, framing it as helpful, not evasive:
“That’s a great question for the doctor during your visit. I’m here to get you scheduled. Does this time work?”
The conversation flow became simple:
- Greet warmly.
- Ask what they need.
- Find a slot.
- Confirm.
- Done.
Goal: Get them booked in under two minutes.
The Transformation
Same fever scenario, second attempt:
The AI acknowledged the concern, offered an appointment time, confirmed details, and finished the call in under 30 seconds. No lectures, no protocol explanations.
Chest‑pain call:
The AI recognized urgency, offered an immediate slot or a nurse transfer, and scheduled appropriately. Knowledge was used only to classify urgency, not to explain cardiac care.
That distinction changed everything.
The Hardest Part: Teaching It to Shut Up
- Telling it not to over‑explain didn’t work.
- Asking it to be concise didn’t work.
- Defining it as a receptionist started working.
- Explicitly stating that explaining medical concepts equals failure finally made it click.
The breakthrough was framing the problem as role boundaries, not knowledge boundaries.
Edge Cases That Taught Me the Most
| Situation | Winning Move |
|---|---|
| Worried patient needs reassurance | Acknowledge → redirect → schedule |
| Insurance question | Acknowledge → give brief logistics → hand off |
| Patient wants to discuss medical research | Acknowledge curiosity → “Great question for the doctor” → schedule |
In every case, the pattern was acknowledge, redirect, schedule.
The Results That Saved My Friday Nights
| Metric | Before Role‑Based Filtering | After Role‑Based Filtering |
|---|---|---|
| Average call time | >2 minutes (often >1 minute of lecture) | <30 seconds |
| Abandonment rate | High (multiple‑digit %) | Single‑digit % |
| Booking success rate | Low | Dramatically higher |
| Patient feedback | “AI talked too much” | “Felt like talking to a real receptionist” |
The client asked why we didn’t do this from the start. I told them the truth: knowledge is not the same as wisdom.
What I Actually Learned
Having access to information is not the same as knowing when to use it.
Defining a clear role for the AI—and limiting knowledge use to that role—turns a noisy know‑it‑all into a helpful, efficient receptionist.
The Prompt Principle I Live By Now
The best prompt isn’t the one that lets the AI show how much it knows.
It’s the one that makes the AI feel effortlessly helpful without trying to impress anyone—like a great receptionist: warm, efficient, and knowing exactly when to speak and when to just get you scheduled.
Key Takeaways
- Focus over breadth – Less context often produces better outcomes. Real users will always expose flaws that ideal test conversations hide.
- Define the AI’s job in one sentence – Treat this definition like a law. Be ruthless about what knowledge is essential and what is just noise.
- Plan for out‑of‑scope queries – Decide in advance how the AI should redirect when users ask outside its role.
- Measure success by outcomes – Not by how smart the AI sounds.
Your Turn
- Have you ever built an AI that knew too much for its own good?
- How do you manage knowledge‑base scope in your prompts?
- What’s your strategy for keeping AI responses focused when you’ve given it access to massive information?
Written by Farhan Habib Faraz
Senior Prompt Engineer & Team Lead at PowerInAI
Building AI that knows when to shut up and just do the job.
Tags: knowledgebase, promptengineering, voiceai, rag, aidesign, llm