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Prompt Engineering Guide

Mastering Write SQL query
on Llama 3.1 70B

Stop guessing. See how professional prompt engineering transforms Llama 3.1 70B's output for specific technical tasks.

The "Vibe" Prompt

"Write a SQL query that retrieves all orders placed by customers in 'New York' and have a total amount greater than $100.00. The database has 'orders' and 'customers' tables. 'orders' has columns: 'order_id', 'customer_id', 'order_date', 'total_amount'. 'customers' has columns: 'customer_id', 'name', 'city'."
Low specificity, inconsistent output

Optimized Version

STABLE
You are a highly-skilled SQL query generator. Your task is to write a SQL query based on the provided schema and requirements. Follow these steps: 1. Understand the goal: Retrieve specific orders. 2. Identify necessary tables: 'orders' and 'customers'. 3. Determine join conditions: 'customers.customer_id = orders.customer_id'. 4. Apply filtering conditions: - Customers in 'New York': 'customers.city = 'New York'' - Orders with total amount > $100.00: 'orders.total_amount > 100.00' 5. Select required columns (implicitly all from orders for this request). Schema: Customers Table: - customer_id (PK) - name (VARCHAR) - city (VARCHAR) Orders Table: - order_id (PK) - customer_id (FK to Customers) - order_date (DATE) - total_amount (DECIMAL) Request: Retrieve all orders placed by customers located in 'New York' that have a total amount exceeding $100.00.
Structured, task-focused, reduced hallucinations

Engineering Rationale

The 'optimized_prompt' enhances clarity and guidance through explicit step-by-step instructions (chain-of-thought). It clearly segments the schema from the request and specifies the role, leading to a more structured and accurate response. The prompt guides the model to break down the problem into logical parts—identifying tables, join conditions, and filtering conditions—which mirrors how a human expert would approach the task. This reduces ambiguity and the cognitive load on the LLM, making it less likely to misinterpret the request or omit crucial conditions. The clear schema definition also prevents misunderstandings about column names or types.

0%
Token Efficiency Gain
The generated SQL query must include a JOIN between 'customers' and 'orders' tables.
The WHERE clause must filter for 'customers.city = 'New York''.
The WHERE clause must filter for 'orders.total_amount > 100.00'.

How We Validate This Prompt

Every optimized prompt for Write SQL query on Llama 3.1 70B is scored against a fixed set of evaluation assertions. A revision ships only when it passes all of them, so the 0% token reduction never comes at the cost of output quality.

  • The generated SQL query must include a JOIN between 'customers' and 'orders' tables.
  • The WHERE clause must filter for 'customers.city = 'New York''.
  • The WHERE clause must filter for 'orders.total_amount > 100.00'.
  • The SELECT statement should retrieve appropriate columns, ideally all from the 'orders' table as per the request.

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Optimize your own Write SQL query prompt

Run any prompt through the same optimizer that produced this Llama 3.1 70B guide — clarity, structure, and token efficiency in one pass.

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