Prompt Engineering Guide for SMEs: The Most Valuable AI Skill You're Overlooking

It's not about AI hype. It's about making AI actually work in your business.

At Axion Lab, we help European SMEs use AI to solve real problems: from automating repetitive tasks to turning messy data into decisions. Yet there's one invisible layer that makes this possible: prompt engineering.

Prompt engineering = the skill of giving AI the right instructions to make it useful for your business.

This guide breaks down:

• What prompt engineering really is
• Why it matters for non-technical business teams
• How Axion Lab applies it to automate processes for SMEs across Europe

From Business Pain to Useful Output: Why Prompts Matter

Prompting is not about clever phrasing. It's about understanding your process well enough to teach it to a machine.

Imagine you're trying to:

• Create a sales report from 10 Excel files
• Answer 50 customer questions in a day
• Draft instructions and guidelines without rework

In order to work efficiently, you have to tell it exactly what to do, in the right structure.

E.g., it helps our team translates real-world and sometimes vague goals into logical instructions for AI. We architect conversations and workflows that combine logic, tools, and custom data.

The Core Prompting Frameworks We Use At Axion Lab

Just like good management has structure, so does good prompting. Below are the 5 foundational techniques we use: each useful in different business automation scenarios.

1. Chain of Thought (CoT)

→ Make the AI reason step-by-step like a human

Instead of jumping to answers, the AI walks through its logic. This boosts reliability, especially for multi-step or conditional logic.

Example use cases:

• Approving expense reports with multiple criteria
• Summarizing meeting transcripts with context retention
• Drafting multi-step emails (e.g. follow-ups + suggested next actions)

Let's break this down step by step.
Step 1: ...
Step 2: ...
Final Answer: ...

2. ReAct (Reason + Act)

→ Let the AI think, then take action

ReAct helps AI models alternate between thinking and doing. It's great when we combine AI with tools (e.g. file search, calendar access, CRMs).

Example use cases:

• Pulling product info from Notion or SharePoint
• Fetching client details from a CRM
• Suggesting next actions based on task descriptions

Format:

Thought: I need more info.
Action: Search["Invoice #245"]
Observation: Found invoice for €1,800 dated June 2.
Thought: Based on this, proceed with payment approval.

3. Tree of Thought (ToT)

→ Explore multiple paths before choosing

Rather than one answer, the AI generates and compares several options. Useful when decisions have tradeoffs.

Example use cases:

• Choosing the best sales pitch from 3 drafts
• Comparing formats for a report or newsletter
• Exploring different ways to word a client email

Format:

Option A: ...
Option B: ...
Option C: ...
Evaluation: Option B is the clearest and most persuasive.

4. RAG (Retrieval-Augmented Generation)

→ Ground the AI in your own files or knowledge base

Instead of generating generic text, the AI searches a document base first, then responds. This keeps answers accurate and company-specific.

Example use cases:

• Answering internal process questions from your SOPs
• Drafting responses based on past proposals or reports
• Summarizing internal documentation for onboarding

We combine RAG with custom tools to search your folders, Notion, or Google Drive.

5. Few-Shot Prompting

→ Teach the AI by example

Rather than give rules, we show the AI 2–5 examples and it learns the format.

Example use cases:

• Classifying incoming requests (e.g. billing vs. support)
• Extracting structured data (e.g. date, amount, company)
• Creating summaries or labels from long text

Format:

Example 1: ...
Example 2: ...
Now for this new input: ...

The Axion Lab Difference: Prompting with Business Impact

We don't stop at "good prompts." We:

• Understand your business logic, then encode it
• Test prompts inside real workflows
• Continuously optimize outputs for speed, clarity, and cost

Our clients don't care about academic prompt theory. They care that:

• A process that took 4 hours now takes 20 minutes
• A junior employee can now do what only seniors did before
• Teams waste less time on admin, and more time creating value

We're tool-agnostic. What matters is what fits your setup, not what's trendy. Also, we continuously test difference tools and this list can be already outdated if you are reading this article one month after it's been published

• ChatGPT → great for general brainstorming and reasoning
• Claude → better for coding
• Custom workflows → integrated with Notion, Google Docs, CRMs, etc.

Prompt engineering = Your New Ops Layer

At Axion Lab, we treat prompt engineering like operational logic design, the bridge between:

• Your team's domain knowledge
• The data you already have
• The outputs that actually drive your business

We use prompts to build:

• AI copilots that guide your team
• Automated workflows across tools
• Assistants that take care of the repetitive stuff

TL;DR

Treat prompt engineering as a natural human language to communicate with AI and explain it what to do. This is the tool for any decision-makers. Long-term strategy becomes an immediate action when you add AI to the mix.

At Axion Lab, we use prompt engineering to:

• Save time on reporting, documentation, and analysis
• Build internal copilots and smart internal assistants
• Turn data and workflows into business leverage

Ready to implement AI in your business? Contact Axion Lab to discover how prompt engineering can transform your operations and unlock hidden productivity gains.