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Prompt Engineering for ERP Logic: How to Talk to Your AI Agent

· 4 min read
Gabriel Paunescu
Gabriel Paunescu
Founder CTO Neologic

The difference between a hook that needs 20 minutes of fixing and one that's merge-ready comes down to how you write the prompt. Garbage in, garbage out — but structured prompts unlock structured code.

The Premise​

Logic Bee's AI agent has skills — structured knowledge files about the codebase's conventions, patterns, and tools. But skills are only activated when the AI recognizes them as relevant. A vague prompt activates vague knowledge. A precise prompt activates exactly the right skill at the right depth.

The Story​

Two developers need the same hook: a function in the inventory-management library that adjusts stock levels after a sales order is confirmed. Here are their prompts:

The Vague Prompt​

"Create a hook that updates inventory when an order is placed."

The AI generates a functional but generic hook. It uses a basic query pattern, doesn't reference the correct collection names, and invents a field structure that doesn't match the actual schema.

The Precise Prompt​

"Create a new hook in inventory-management called adjust-stock-on-order-confirm. When a sales order status changes to 'confirmed', decrement the stock quantity for each line item. Use flowQuery to look up items by SKU in the inventory/items collection. Throw a bad_request error if any item has insufficient stock. Use the creating-hooks, flow-query, and hook-code-style skills."

The AI generates code that follows every convention, queries the right collections, handles edge cases, and validates inputs.

The Anatomy of an Effective Prompt​

1. Name the Library and Method​

Always specify where the hook lives. The AI uses this to set the file path, decorator metadata, and naming conventions.

❌ "Create a hook that..."
✅ "Create a new hook in finance-bills called calculate-late-fees..."

2. Describe the Trigger​

ERP logic doesn't exist in isolation. Tell the AI when this logic runs.

❌ "Update inventory"
✅ "When a sales order status changes to 'confirmed'"

3. Specify Collections and Fields​

The AI will guess field names if you don't provide them, and it will guess wrong.

❌ "Look up the product"
✅ "Use flowQuery to look up items by SKU in the inventory/items collection"

4. Define Error Conditions​

Guard clauses and error handling are where AI-generated code is weakest. Explicitly state what should fail and how.

❌ "Handle errors"
✅ "Throw a bad_request error if any item has insufficient stock"

5. Reference Skills by Name​

This is the multiplier. Skill references tell the AI exactly which knowledge to load.

❌ (no skill reference)
✅ "Use the creating-hooks, flow-query, and hook-code-style skills"

Prompt Template​

Here's a copy-paste template for creating hooks:

Create a new hook in [library-slug] called [method-slug].

When [trigger condition], [primary action].
Use flowQuery to [query description] in the [collection-path] collection.
[Additional business rules].
Throw a [error-type] error if [failure condition].

Use the creating-hooks, flow-query, and hook-code-style skills.

Example using the template:​

Create a new hook in shipping-management called calculate-shipping-rates.

When a sales order is ready for fulfillment, calculate shipping rates 
for all available carriers. Use flowQuery to look up the order's line 
items in the sales/orders collection and the customer's shipping address 
in the contacts/addresses collection. Apply weight-based rate tiers 
from the shipping/rate-tables collection. Throw a bad_request error if 
the shipping address is missing or if total weight exceeds carrier limits.

Use the creating-hooks, flow-query, and hook-code-style skills.

Advanced Techniques​

Ask for a Pattern Search First​

Before generating new code, ask the AI to find similar existing hooks:

"Search for hooks that match the pattern 'calculate' in the finance-bills library."

This gives the AI a concrete reference implementation, producing much better results than generating from skills alone.

Chain Prompts for Complex Hooks​

For hooks with multiple steps, break the work into sequential prompts:

  1. "Scaffold the hook structure for..."
  2. "Now implement the query logic using..."
  3. "Add validation for..."

Specify What NOT to Do​

Negative constraints are surprisingly effective:

"Do not fetch all documents — use pagination. Do not calculate totals in JavaScript — use a database aggregation pipeline."

The Takeaway​

Effective prompts for ERP logic follow a formula: name the location, describe the trigger, specify the data, define the errors, and reference the skills. The five minutes you spend writing a precise prompt save thirty minutes of fixing vague output.

Your prompt is your spec. Write it like one.