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Chatbot Gives Wrong Answers: 5 Causes and How to Fix Each One

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Your chatbot is live — but it’s giving incorrect, outdated, or hallucinated answers. This is one of the most common chatbot issues and almost always has a specific, fixable cause. This guide walks through each cause systematically so you can identify and fix the problem fast.

Diagnose the Type of Wrong Answer First

Not all wrong answers have the same cause. Identify which type you’re seeing:

Type Example Most likely cause
Hallucinated information States a policy or price that doesn’t exist AI using general knowledge instead of training data
Outdated information States old prices, discontinued products, changed policies Training data not updated after changes
Wrong but related Answers a similar question, not the actual question asked Training data structure or retrieval settings
Too vague / generic “We have great products and excellent service” Missing training data or wrong knowledge base connected
Contradictory Gives different answers to the same question in different sessions Conflicting information in training documents

Fix 1: AI Is Using General Knowledge Instead of Your Training Data

Symptoms: Answers sound plausible but are made-up. Policies cited don’t exist. The chatbot acts confident about things it couldn’t know.

Cause: Your system prompt doesn’t restrict the AI to your knowledge base, or no knowledge base is connected.

Fix:

  1. Go to your chatbot’s Context tab
  2. Add this restriction to your system prompt: “Only answer questions using information from the knowledge base. If the information is not in the knowledge base, say: ‘I don’t have that information — please contact us at [email].’ Never make up policies, prices, or product details.”
  3. Go to the Knowledge tab and verify that your dataset is toggled on
  4. Save and test again

Fix 2: Training Data Is Outdated

Symptoms: Chatbot states old prices, references discontinued products, or gives a policy that changed 3 months ago.

Cause: Training data was not updated when your business information changed.

Fix:

  1. Go to AI Training → your dataset
  2. Find and edit the documents with outdated information
  3. Click Re-generate Embeddings for each updated document
  4. Test the specific questions that were giving wrong answers
💡 Prevention: Add a calendar reminder whenever you change prices, policies, or products to also update your training data. See Training Data Best Practices for how to structure content for easy maintenance.

Fix 3: Wrong Answer Retrieved (Search Quality Issue)

Symptoms: The chatbot answers a related but different question. Seems to “miss the point” of what was asked.

Cause: The semantic search is returning a loosely related document chunk instead of the specific one that answers the question.

Fix options:

  • Restructure training data: Split large documents into smaller, topic-focused ones. Add Q&A format for FAQ-style topics. See Training Data Best Practices.
  • Lower similarity threshold: Go to the chatbot’s Knowledge tab → set threshold to 0.6 (retrieves more results, more likely to include the right one)
  • Increase results per query: Change from 3 to 5 — the AI sees more context and may find the right information
  • Switch to Hybrid search mode: Combines semantic + keyword search for better precision on specific questions

Fix 4: No Training Data Connected

Symptoms: Generic, unhelpful answers. The chatbot has no specific knowledge of your business.

Fix:

  1. Go to the chatbot’s Knowledge tab
  2. Check that a dataset is toggled on
  3. If no datasets exist yet: follow the Embeddings in 10 Minutes guide to create one

Fix 5: Conflicting Information in Training Data

Symptoms: Chatbot gives different answers to the same question in different sessions. Sometimes correct, sometimes wrong.

Cause: Multiple documents contain contradictory information (e.g. return policy is 30 days in one document and 14 days in another).

Fix:

  1. Search your dataset for the topic causing issues
  2. Review all documents mentioning that topic
  3. Remove or update any that contain outdated/contradictory information
  4. Keep only one authoritative document per policy/topic
  5. Re-generate embeddings

Quick Diagnostic Checklist

Work through this list in order when a chatbot gives wrong answers:

  1. ☐ Is a dataset connected and toggled on? (Knowledge tab)
  2. ☐ Does the system prompt restrict the AI to the knowledge base?
  3. ☐ Is the relevant information actually in the training data? (Search your dataset directly)
  4. ☐ Is the training data current? (Check dates on policy documents)
  5. ☐ Are there conflicting documents on this topic?
  6. ☐ Are the retrieval settings appropriate? (threshold, results count, search mode)
✅ Wrong answers fixed? Most chatbot accuracy issues resolve when the knowledge base is well-structured, current, and the system prompt properly restricts the AI to use it.

What’s Next

Last verified: AIWU v.4.9.2 · Updated: 2026-02-25

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