The Simple Guide to Customising AI for Retail: Prompts, RAG, and Fine-Tuning Explained

AI models like ChatGPT are powerful, but they’re not one-size-fits-all. Depending on what you need them to do, whether that’s answering customer questions, analysing product data, or helping your team make faster decisions, there are a few ways to tailor them for the real world. 

In this post, we’ll break down three main approaches, prompt engineering, fine-tuning, and RAG (Retrieval-Augmented Generation). We’ll explain what they are, when to use each one, and what to watch out for. We’ll also share how your approach might evolve as your AI product grows or your business needs change. 

Different Ways to Tailor AI Models For Your Retail Use Case

AI models like ChatGPT can be adapted in a few different ways depending on what you need them to do. Here’s what each approach actually means. 

Full pre-training 

This is where you build a large language model entirely from scratch, training it on massive amounts of data (think the entire internet). It’s what big tech companies like OpenAI or Google do to create new foundation models. For everyone else, it’s usually far too expensive and unnecessary. 

Fine-tuning 

Fine-tuning means taking an existing AI model and teaching it to do something new using your own examples. For example, you might train it to use your company’s tone of voice, understand your product catalogue, or automate a specific business task. It’s like giving the model a focused “education” on your world. 

Prompt engineering 

Prompt engineering doesn’t involve training the model at all. Instead, it’s about learning how to ask it the right questions or give it better instructions. For example, you might say “Summarise this report in three bullet points for a store manager” instead of just “Summarise this report.” It’s quick, cheap, and ideal for testing ideas fast. 

RAG (Retrieval-Augmented Generation) 

RAG connects an AI model to your company’s data, like spreadsheets, reports, or knowledge bases, so it can find up-to-date information and include it in its answers. Think of it like giving the model access to your internal files instead of expecting it to “remember” everything. 

Summary

Approach	What actually changes	How much data you need	Cost & effort	When it makes sense
Full pre-training	You’re building a model completely from scratch.	Massive amounts of data (terabytes).	Very expensive and time-consuming. Only big tech companies do this.	Building your own version of ChatGPT – not realistic for most businesses.
Fine-tuning	You start with an existing model and teach it new things using examples.	A few hundred to a few thousand examples.	Medium effort – hours to days depending on setup.	When you want your AI to sound like your brand, understand your products, or automate specific tasks.
Prompt engineering	You don’t train the model, you just ask better questions or give clearer instructions.	Almost no data. Maybe a handful of examples.	Very low cost and quick to test.	Perfect for quick pilots or when rules change often.
RAG (Retrieval-Augmented Generation)	The model stays the same, but it looks up your latest company data each time it answers.	Your own data (files, reports, spreadsheets, etc.).	Moderate – some setup to connect your data.	Great for keeping answers fresh, accurate, and easy to trace.

The Trade-Offs at a Glance: Choosing the Right AI Approach for Retail

When it comes to adapting AI models, there’s no one-size-fits-all. Each method has pros and cons depending on what you’re trying to achieve and how much time, budget, and data you’re working with. 

Let’s break it down. 

AI Cost and Speed: Balancing Budget and Performance

• Prompt engineering: It’s free to start, but every time you use it, you’re paying for the full set of instructions you include in the prompt. The more detail you add, the more tokens (and cost) it uses and responses can take a little longer. 

• Fine-tuning: Once trained, your AI doesn’t need long prompts. It already “knows” how to respond, which can cut token costs by up to 90% and make it faster. 

• RAG: Slightly more expensive to run because it searches your data each time, but you don’t need to keep retraining it and it always uses the latest information. 

AI Performance and Reliability: How Consistent Are the Results?

• Prompt engineering: Works well for most tasks, but responses can shift when the underlying model updates or when something unusual pops up. 

• Fine-tuning: Much more stable. Once the behaviour is trained in, it stays consistent in tone, structure, even how it handles tricky edge cases. 

• RAG: Pulls from real company data, so it’s less likely to make things up but if your data is messy or unclear, it can still produce the wrong answer. 

Keeping Your AI Up to Date: How Easy Is It to Maintain?

• Prompt engineering: Super easy to tweak. You can test and adjust the wording anytime, which is great for quick experiments. 

• Fine-tuning: Very reliable once set up, but takes more effort to update if your tone, policies, or product details change. 

• RAG: The simplest to keep fresh; just update your documents or database, and the AI automatically starts using the new information. 

Tools That Make AI Testing and Management Easier

• Prompt engineering: Easy to play around with using tools like ChatGPT Playground, or LangSmith – perfect for quick testing. 

• Fine-tuning: Platforms like OpenAI, Amazon Bedrock, or Google Vertex make it easy to upload your data and fine-tune models with just a few clicks. 

• RAG: Tools like Pinecone, Weaviate, and Milvus handle the data search part, and most modern AI platforms now include RAG features out of the box. 

In Short: When to Use Prompts, Fine-Tuning or RAG in Retail

If you want to experiment quickly, start with prompt engineering. 
If you need consistency and brand alignment, fine-tuning is worth it. 
And if your business depends on up-to-date information, RAG keeps your AI grounded in real data. 

Real-World AI Use Cases in Retail: Four Practical Examples

Here’s how different AI approaches can be used in real retail scenarios, from speeding up customer service to keeping marketing content fresh. 

Use case	Core goal	Techniques used
Product Data Validator: Using AI to Keep Product Information Accurate and Up to Date	Automatically check product descriptions, pricing, and stock information for missing or inconsistent details across multiple systems.	Start with simple prompts to test the idea. Then use prompt chaining to extract and validate product details. As the data volume grows, fine-tune to improve accuracy and reduce manual checks.
Customer Service Assistant: Smarter Support Powered by Real-Time Data (RAG Example)	AI-powered chatbot that answers questions about orders, delivery times, and returns pulling info directly from internal systems like Shopify or ERP.	Use RAG to access real-time product and order data, ensuring answers are current. No fine-tuning needed since product and policy data changes daily.
Sales Insights Query Tool: Asking Natural-Language Questions About Store Performance	Let store managers and merch teams ask natural-language questions like “Which products sold best last week in Manchester?” and get instant insights from sales databases.	Start with a few sample prompts but you may hit syntax errors. Fine-tune the model using real sales query examples to dramatically improve accuracy and reduce errors.
Personalised Marketing Content Generator: Creating On-Brand Campaigns at Scale	Automatically create weekly promotional emails or social posts tailored to each customer segment using current offers and products.	Combine RAG to pull in the latest offers with light fine-tuning to ensure the AI always writes in the brand’s tone of voice. The hybrid approach keeps content both relevant and on-brand.

These examples show how retailers can start small, testing ideas with prompt engineering and evolve to more advanced setups with fine-tuning or RAG as data and complexity grow. 

How to Choose the Right AI Approach: From Idea to Rollout

When you’re building an AI solution, the best approach often depends on where you are in the journey; from a quick prototype to a live, scaled product. You don’t need to start big; it’s better to test fast, learn, and then optimise as you go. 

Stage	What you’d typically use	Why it fits
Start Small: Use Prompts to Test Ideas Quickly	Start with simple prompts and maybe a small RAG setup pulling from a few documents.	Fastest way to get early feedback; no big data prep or tech setup needed.
Add RAG When You Need Reliable, Up-to-Date Information	Add a few more structured prompts, use RAG to pull from real company data, and set some basic guardrails for quality.	Adds consistency and reliability while keeping things flexible so you can adjust as you learn what works.
Fine-Tune Once You’re Ready to Scale or Standardise	Fine-tune for the key tasks that are now stable, keep RAG to ensure fresh info, and refine your prompts for the best user experience.	Reduces costs, speeds up responses, and locks in your preferred tone and accuracy while still staying up to date.

Quick rules of thumb 

1. Start with prompts – It’s the fastest way to test ideas and spot what you don’t know yet. 

2. Add RAG when the AI lacks facts – If it starts guessing or missing detail, connect it to your real data. 

3. Fine-tune once things stabilise – When you’ve got consistent use cases or compliance needs, fine-tuning makes your AI faster, more reliable, and more on-brand. 

The Tools That Make It Happen: Platforms for Prompts, Fine-Tuning and RAG

Once you start experimenting with AI, a few tools can make your life a lot easier especially when it comes to testing prompts, tracking results, and managing costs. 

Prompt Engineering Tools: Test and Improve Your AI Responses

These tools help you design, test, and refine how your AI responds, a bit like tuning a digital assistant until it sounds just right. 

• LangSmith / LangChain Hub – Great for experimenting with different prompts, comparing versions, and seeing which performs best. 

• OpenAI Playground & Azure OpenAI Studio – Simple, hands-on environments where you can test prompts and instantly see how the model responds. 

• PromptLayer / Portkey – Useful for tracking how your prompts perform over time and keeping an eye on token usage and costs. 

Fine-Tuning Platforms: Train AI to Match Your Brand and Workflow

If you get to the stage where you want your AI to be more reliable, on-brand, or better trained on your business data, there are plenty of platforms that make fine-tuning easy.

Here’s a quick overview of the main players and what they’re best at: 

Platform	What it offers	Why it matters
OpenAI	Models like GPT-3.5-Turbo and GPT-4o-mini can be fine-tuned with just one command.	Simple and fast – ideal if you’re already using ChatGPT and want to make it fit your brand tone or workflow.
Azure OpenAI	Uses the same OpenAI models but with Microsoft’s enterprise security and compliance.	Perfect for retailers already running on Microsoft – keeps everything within your organisation’s governance setup.
Amazon Bedrock	Works with models like Llama, Titan, and Cohere.	Gives you flexibility to test multiple models and fine-tune them inside AWS, alongside your existing data.
Google Vertex AI	Uses Gemini and PaLM models with a built-in “tuning wizard.”	Great if your data lives in Google Cloud and you want a simple interface to manage training.
Hugging Face	Open-source platform that supports popular models like Llama and Mistral.	Good for teams that want control and flexibility without vendor lock-in.
Databricks MosaicAI	Fine-tunes models like Llama and MPT using scalable cloud infrastructure.	Ideal for data-heavy businesses that already use Databricks for analytics.
NVIDIA NeMo	Designed for high-performance use cases, with multiple tuning options.	Powerful option if you’re working with large or complex datasets.
Cohere	Focuses on efficiency – its models are up to 15x cheaper to run than huge ones.	A smart choice for cost-conscious businesses who still want quality results.

Fine-tuning is no longer just for tech giants. Most platforms now make it as easy as uploading your data and clicking a few buttons. For retailers, it’s a practical next step once you know what you want your AI to do consistently, whether that’s writing product descriptions, answering customer queries, or spotting stock issues. 

Key Takeaways: How to Build a Smart AI Strategy for Retail

Here’s the simple way to think about it: 

Prompt Engineering: Best for Speed and Flexibility

Great for testing ideas quickly or when things change often, like tweaking store promotions or experimenting with customer messages. 

Fine-Tuning: Best for Consistency and Scale

Best when you need your AI to behave the same way every time. Perfect for scaling customer service, automating reporting, or following brand tone across hundreds of stores. 

RAG: Best for Fresh, Reliable Information

Ideal when your business data changes daily, like product availability, pricing, or policies and you want the AI to stay accurate without retraining it every week. 

The best approach usually mixes all three: 
Start with prompts to move fast, 
add RAG when you need reliable facts, 
and fine-tune once you’re ready to scale and polish the results. 

That’s how you go from quick AI prototypes to real, production-ready tools that save time, reduce costs, and actually make a difference in the day-to-day running of your retail business.