Strategies to Analyze Any Size Dataset with LLMs Beyond its Context Limit

You've connected your LLM to a SQL database using Model Context Protocol (MCP), run a query, and... oh no. Instead of the manageable 100 rows you expected, you get 50,000 records. Your LLM's context window can only handle a fraction of this data. What now?

Here's how to fix this, starting with the most effective approaches.

The Summary Approach

Instead of dumping raw data, provide strategic summaries that give the LLM (and your users) the big picture.

SELECT 
    COUNT(*) as total_records,
    AVG(sales_amount) as avg_sale,
    MIN(sales_amount) as min_sale,
    MAX(sales_amount) as max_sale,
    COUNT(DISTINCT customer_id) as unique_customers,
    SUM(sales_amount) as total_revenue
FROM sales_data 
WHERE date >= '2024-01-01';

The LLM gets the complete statistical picture in a few dozen tokens instead of thousands. It can identify outliers, understand distributions, and ask intelligent follow-up questions.

Progressive Disclosure Pattern

Think of this as a conversation rather than a data dump:

1. High-level summary: We have 50,000 sales records totaling $2.4M
2. Key trends: Sales are growing 15% month-over-month, with Electronics being the top category
3. Segment analysis: Let me analyze the top 10% of customers who drive 40% of revenue
4. Deep dive: Show me the actual transactions for our VIP customers last month

Each step is context-aware and builds on previous understanding.

Intelligent Sampling

Not all data points are created equal. Instead of random sampling, use strategic approaches:

SELECT * FROM (
    SELECT *, 
           ROW_NUMBER() OVER (PARTITION BY category ORDER BY RANDOM()) as rn
    FROM products
) WHERE rn <= 10;  -- 10 from each category

-- Importance sampling: focus on what matters
SELECT * FROM customers 
ORDER BY lifetime_value DESC, last_purchase_date DESC
LIMIT 100;

Time-based Chunking

Large datasets often have a time component. Use this to your advantage:

SELECT 
    DATE_TRUNC('week', order_date) as week,
    COUNT(*) as order_count,
    AVG(order_amount) as avg_order_size
FROM orders
GROUP BY DATE_TRUNC('week', order_date)
ORDER BY week;

This helps identify trends, seasonality, and anomalies without overwhelming the context window.

The Outlier-First Method

Often, the most interesting insights live in the extremes:

SELECT *
FROM (
    SELECT *,
           (sales_amount - AVG(sales_amount) OVER()) / STDDEV(sales_amount) OVER() as z_score
    FROM sales_data
) WHERE ABS(z_score) > 3;

The goal isn't to show all your data, it's to derive insights from it. By using these strategies, you transform from someone who struggles with context limits to someone who delivers precise, actionable insights regardless of data size.