Preswald | Blog

AI chatbots are at the core of customer support, financial dispute resolution, and automated workflows. But when they fail - misclassifying intents, generating incorrect responses, or responding too slowly - debugging the issues can be frustrating and time-consuming.

With Preswald, you can analyze chatbot logs, uncover key insights, and troubleshoot performance issues in an interactive data app.

Your fintech chatbot handles fraud dispute reports, predicting user intent (e.g., fraud dispute, transaction check, account lock). Here’s what can go wrong:

🔴 Misclassified intents – AI assigns the wrong category to a user request
🔴 Slow response times – Chatbot responses take too long
🔴 High escalation rates – AI fails to resolve cases, requiring human intervention
🔴 Poor response quality – AI replies are vague, inaccurate, or misleading

Step 1: Install and Set Up Preswald

pip install preswald

Initialize a new Preswald project:

preswald init chatbot_debugger
cd chatbot_debugger

This creates a boilerplate project with all necessary config files.

Step 2: Load and Explore Chatbot Logs

Your chatbot logs user queries in a structured format like this:

Timestamp	User_ID	Query	Predicted_Intent	AI_Response	Response_Time (s)	Escalated	Sentiment	Resolution_Status
2025-03-10 14:12	10432	"I didn’t authorize this charge."	Fraud_Dispute4	"Disputing the charge now."	1.2	No	Positive	Resolved
2025-03-10 14:15	20411	"Someone stole money from my account!"	Account_Lock	"Please verify your identity."	2.8	Yes	Negative	Escalated
2025-03-10 14:20	30521	"Was my card used at a gas station?"	Transaction_Check	"You can view your transactions in the app."	1.5	No	Neutral	Resolved

Connect to Data Source

Add your chatbot logs as a data source in preswald.toml:

[data.chatbot_logs]
type = "csv"
path = "data/chatbot_logs.csv"

Then, load the dataset in your app:

from preswald import connect, get_df, table

connect()  # Load data sources from preswald.toml
df = get_df("chatbot_logs")
chatbot_logs = get_df("chatbot_logs")  # Load chatbot logs as DataFrame
table(chatbot_logs, title="Chatbot Logs")

Step 3: Identify and Debug Chatbot Failures

1️⃣ Detect Misclassified Intents

Analyze how often the chatbot assigns incorrect intents.

from preswald import query, table

sql = """
    SELECT 
        Predicted_Intent, COUNT(*) as Count
    FROM chatbot_logs
    GROUP BY Predicted_Intent
    ORDER BY Count DESC
"""
intent_distribution = query(sql, "chatbot_logs")
table(intent_distribution, title="Chatbot Intent Distribution")

💡 Fix: If fraud-related queries are often classified as "Account Lock," retrain the AI model with better labeled data.

2️⃣ Measure Chatbot Response Times

Identify cases where the chatbot is too slow.

from preswald import query, plotly
import plotly.express as px

sql = """
    SELECT Response_Time FROM chatbot_logs
"""
response_times = query(sql, "chatbot_logs")

fig = px.histogram(response_times, x="Response_Time", title="Chatbot Response Time Distribution")
plotly(fig)

💡 Fix: If response times exceed 2 seconds, optimize database calls, model inference, or server performance.

3️⃣ Track Escalation Rates

Measure how often the chatbot fails to resolve cases and escalates to human agents.

sql = """
    SELECT 
        Escalated, COUNT(*) as Count
    FROM chatbot_logs
    GROUP BY Escalated
"""
escalation_stats = query(sql, "chatbot_logs")
table(escalation_stats, title="Chatbot Escalation Rate")

💡 Fix: If escalation rates are high, improve chatbot decision-making by fine-tuning the AI model.

4️⃣ Evaluate AI Response Quality

Check sentiment analysis on chatbot responses to gauge user satisfaction.

sql = """
    SELECT Sentiment, COUNT(*) as Count
    FROM chatbot_logs
    GROUP BY Sentiment
"""
sentiment_stats = query(sql, "chatbot_logs")
table(sentiment_stats, title="User Sentiment Distribution")

💡 Fix: If many responses are negative, improve response quality by adding more empathetic, context-aware replies.

Step 4: Deploy and Share Your Debugging App

Once your chatbot debugging dashboard is ready, deploy it:

preswald deploy --target structured --github <github-username> --api-key <structured-api-key>

🔗 This creates a live dashboard where your team can monitor chatbot performance and debug failures in real-time.

With Preswald, you can quickly:

Load chatbot logs into an interactive dashboard
Analyze misclassifications, response times, and escalation rates
Detect negative sentiment and improve chatbot responses
Deploy a cloud-based debugging app in one command

Get started now! https://github.com/StructuredLabs/preswald

How to debug AI chatbots with Preswald