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Ask Mode

No notebook changes — the AI answers questions about your notebook, explains code, and provides guidance without modifying anything.
Best for: Exploration, explanations, learning, and getting advice without altering your cells. Perfect when you want to understand before you act.

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How Ask Mode Works

Ask Mode is purely conversational. The AI has read-only access to your notebook and can help you understand what’s happening without making any changes:
1

AI reads your notebook

The AI can see:
  • All cells and their outputs
  • Current kernel state (variables, dataframes)
  • Database connections
  • Previous execution results
The AI can read everything but cannot modify anything.
2

You ask questions

Ask anything about your notebook:
  • “What does this code do?”
  • “Why is this cell producing an error?”
  • “How can I optimize this query?”
  • “Explain the logic in cell 7”
  • “What’s the difference between these two approaches?”
3

AI provides explanations

The AI responds with:
  • Clear explanations in natural language
  • Code breakdowns and logic walkthroughs
  • Debugging suggestions
  • Performance optimization ideas
  • Alternative approaches
No code is generated or executed. It’s purely advisory.
4

You decide what to do

Armed with the AI’s explanation, you:
  • Manually edit cells based on suggestions
  • Run experiments to test ideas
  • Switch to Hands-On or Agent Mode if you want AI to implement changes

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Example Interactions

You: “What does cell 5 do?”AI: “Cell 5 performs a left join between the customers and orders dataframes on the customer_id column. It then filters for orders from 2024 and groups by region to calculate total revenue. The result is sorted by revenue in descending order.”
Ask Mode is perfect for onboarding to unfamiliar notebooks or understanding code written by teammates.
You: “Why is the last cell showing a KeyError?”AI: “The KeyError on revenue_usd suggests that column doesn’t exist in your dataframe. Looking at cell 3, the column is actually named revenue_dollars. You have two options:
  1. Rename the column in cell 3 to revenue_usd
  2. Update cell 7 to use revenue_dollars
I recommend option 2 to maintain consistency with your earlier analysis.”The AI explains the problem but doesn’t automatically fix it. You decide which approach to take.
You: “How would I calculate a rolling average in pandas?”AI: “To calculate a rolling average in pandas, use the .rolling() method:
df['rolling_avg'] = df['value'].rolling(window=7).mean()
This computes a 7-period moving average. You can adjust window to change the period. For your daily_revenue dataframe, you could do:
daily_revenue['7day_avg'] = daily_revenue['revenue'].rolling(window=7).mean()
Would you like me to explain how rolling windows handle edge cases?”
The AI provides educational explanations without modifying your notebook. Perfect for learning new techniques.
You: “Is there a faster way to do this groupby operation?”AI: “Yes! Your current approach loops through groups, which is slow for large datasets. Here’s a vectorized alternative:Current approach (slow):
for group in df.groupby('category'):
    # process each group
Faster vectorized approach:
df.groupby('category').agg({'revenue': 'sum', 'quantity': 'mean'})
For your dataset with 1M rows, this could be 10-100x faster. The vectorized version lets pandas optimize operations internally instead of Python looping.”The AI explains the optimization but lets you implement it.
You: “Are there any issues with my data that I should be aware of?”AI: “I notice a few potential issues in your sales_data dataframe:
  1. Missing values: 342 rows have null customer_id (8% of total)
  2. Negative revenue: 17 rows have negative revenue values (likely refunds?)
  3. Date range gap: No data between March 15-22, 2024
  4. Outliers: 5 transactions over 100k(99thpercentileis100k (99th percentile is 8,500)
Would you like suggestions on how to handle these issues?”
The AI proactively identifies data quality concerns without making assumptions about how to fix them.

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When to Use Ask Mode

Before making changes

Use Ask Mode to understand the current state before modifying anything. Get a clear picture of what exists, then switch modes to implement changes.

Code review

Review notebooks from teammates or understand legacy code without risking accidental modifications.

Learning new techniques

Ask how to implement something or understand a coding pattern. The AI teaches without writing code for you.

Sanity checks

Verify your logic, check for edge cases, or get a second opinion on your approach.

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Limitations

Ask Mode cannot:
  • Generate new code cells
  • Edit existing cells
  • Execute code
  • Create visualizations
  • Modify kernel state
  • Run database queries
For any of these actions, switch to Hands-On Mode or Agent Mode.

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Switching from Ask Mode

When you’re ready to implement changes, switching is seamless:
When to switch: You understand what needs to change and want to approve each suggestion.What happens:
  • Conversation history is preserved
  • Context from Ask Mode carries over
  • AI starts suggesting code instead of just explaining
Example flow:
  1. Ask Mode: “Why is this slow?”
  2. AI explains the issue
  3. Switch to Hands-On Mode
  4. AI suggests optimized code
  5. You review and approve

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Conversation Continuity

One of Ask Mode’s strengths is that conversations carry context: 
You: "What does this analysis calculate?"

AI: "This calculates monthly recurring revenue (MRR) by summing active subscriptions..."

You: "What's the retention rate?"

AI: "Based on the MRR calculation we just discussed, I can see retention in cell 12..."

You: "How can I improve the accuracy?"

AI: "To improve the MRR accuracy, consider these adjustments..."
The AI remembers the conversation flow and builds on previous answers.
Use Ask Mode for extended exploratory conversations. Once you understand the full picture, switch modes to implement changes efficiently.

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FAQ

Yes, Ask Mode reads your notebook state to answer questions accurately. It can see:
  • Cell contents and outputs
  • Kernel variables and dataframes
  • Database connections
  • Execution history
But as always, SignalPilot follows a zero data retention policy. Your data never leaves your environment.
Yes! If you have database connections configured, Ask Mode can:
  • Describe table schemas
  • Explain relationships between tables
  • Suggest queries
  • Identify performance issues
It can read database metadata but cannot execute queries (read-only mode).
Yes. Ask Mode can:
  • Identify logical errors
  • Spot common pitfalls (e.g., division by zero, type mismatches)
  • Warn about performance issues
  • Suggest edge cases to test
But it won’t automatically fix bugs — it explains them so you can decide how to fix them.
Yes! Ask Mode can read from MCP servers to provide context:
  • dbt model documentation
  • Slack discussion history
  • Jira ticket details
  • Query history
This helps the AI give more informed answers based on your full organizational context.

Learn about MCP

See how SignalPilot connects to external tools via Model Context Protocol
Absolutely! Ask Mode is perfect for:
  • “What would happen if I changed this parameter?”
  • “How would this perform on a larger dataset?”
  • “What’s the trade-off between these two approaches?”
  • “If I modify this cell, what else needs to change?”
The AI can reason about hypotheticals without executing anything.
Generally yes, because:
  • No code generation required
  • No execution overhead
  • Simpler interactions
  • Lighter context processing
Ask Mode responses are typically 2-3x faster than code-generating modes.

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Next Steps

Try Hands-On Mode

Get code suggestions with granular approval control

Try Agent Mode

Let the AI plan and execute full analyses autonomously

Cell-Level Editing

Learn about inline edits and tab autocomplete

5-Minute Quickstart

Complete your first analysis with SignalPilot