Prompt Engineering for Retail Agents: Lessons from the Field
Why Prompt Engineering Matters in Retail
Retail teams are adopting Large Language Models (LLMs) to automate:
Procurement requests (e.g. “What vendors supply eco-friendly fabrics?”)
Product Q&A (e.g. “Is this t-shirt dry-fit?”)
Support workflows (e.g. “Track my order” or “Issue a refund”)
But while LLMs are powerful, they’re not magic, they need the right prompt design to deliver reliable, retail-grade answers.
At ManoloAI, we’ve tested dozens of prompt strategies in real-world workflows. Here’s what we’ve learned.
The Challenge: Hallucinations, Vagueness & Inconsistency
Even the best models can:
Confuse similar product SKUs
Invent vendor details when context is missing
Struggle to follow multi-step instructions (“Summarize vendor terms & flag exceptions”)
What Works:
These are the principles that made a difference for our customers.
1. Give the Model Structure
Before:
“Tell me about this supplier.”
After:
“Using the supplier profile below, summarize the company’s product categories, top clients, and payment terms. Use bullet points.”
Why does this work: LLMs love structure. Using bullet points or json helps provide the clarity and structure to be useful
2. Inject Ground Truth (RAG > Hype)
Before:
“What are the top-selling SKUs in June?”
After:
“Here’s the sales data from June. List the top 5 SKUs by revenue.”
We use Retrieval-Augmented Generation (RAG) to pull facts from the source system and pass it into the prompt. This reduces hallucinations dramatically.
3. Use Role-based Instructions
Before:
“Write a note about this product.”
After:
“You are a retail support agent helping a customer decide between two coats. Recommend the best option based on warmth, price, and style.”
Telling the model the persona she is playing increases consistency and tone alignment.
4. Tips for Procurement
There are situations when you have to provide complex instructions. If you have a complex prompting scenario try and break it down to manageable chunks so the models are more effective.
“Step 1: Review the supplier terms below and highlight any exceptions to our standard 30-day payment policy.
Step 2: Flag suppliers missing contact info.
Step 3: Return findings in a table.”
This “step-by-step” prompt flow reduces ambiguity and increases reliability, especially in procurement audits, legal contract analysis or RFP evaluations.
Here is a sample prompt template that we use and share with our clients often
Use Cases We've Enabled
Vendor scorecard summarization from PDFs
Order issue triage for omnichannel brands
Auto-suggesting category tags for new SKUs
RFP comparison summaries for procurement managers
What We Learned
Start narrow: General prompts lead to general (and risky) outputs
Chain your prompts: Break down big asks into small, repeatable tasks
Always test with real, messy data. Ideal prompts fail fast in production
Want to Build Retail Agents That Actually Work?
ManoloAI designs domain-specific prompt stacks for LLM agents in procurement, merchandising, and support. We combine:
✅ RAG
✅ Embedded business logic
✅ Domain-tuned instruction templates