There is a lot of noise in AI right now.
Every other LinkedIn post is someone claiming they 10xed their revenue with a chatbot, or a founder pitching their “AI-powered” startup that is basically a wrapper around an API call. And somewhere in between all of that, there are real, useful things happening that could genuinely help your business.
The problem is that the useful stuff is buried under layers of jargon. RAG, fine-tuning, prompt engineering, embeddings - these words get thrown around like everyone is supposed to know what they mean. Most people don’t. And that’s fine. Nobody was born knowing what a vector database is.
This post is for business people who want to cut through the noise and understand the actual tools available to them. Not the hype. Not the existential dread. Just the tools.
First, The Foundation
Before we get into the toolkit, we need a quick foundation. If you already know what an LLM is, skip ahead.
Large Language Models are the engines behind tools like ChatGPT, Claude, and Gemini. They are trained on massive amounts of text and they learn patterns in language. When you give them a prompt, they predict what should come next. That’s an oversimplification, but it’s close enough for our purposes.
Here’s the thing that trips people up. LLMs don’t know things the way a database knows things. They don’t look up facts. They generate text that sounds right based on patterns they have seen. Sometimes what sounds right is right. Sometimes it’s not. This distinction matters a lot when you are trying to build something reliable for your business.
Keep this in mind. We’ll come back to it.
Prompt Engineering
Let’s start with the simplest tool in the box.
Prompt engineering is the art of asking an LLM the right question in the right way. That’s it. There’s no software to install. No infrastructure to set up. You are just getting better at talking to the AI.
This sounds trivial. It is not.
The difference between a vague prompt and a well-crafted one is the difference between getting a generic, useless response and getting exactly what you need. Consider the difference between asking “Write me a marketing email” versus “Write a marketing email for our B2B SaaS product that helps mid-size law firms automate document review. The tone should be professional but not stiff. The reader is a managing partner who is skeptical of AI. Keep it under 200 words.”
The second prompt gives the AI context, constraints, audience, and tone. The output will be dramatically better.
There are a few techniques worth knowing. System prompts let you set ground rules before the conversation starts - you could tell it to always respond in a specific format, or to never make up information it doesn’t have. Few-shot prompting is when you show the AI examples of what you want instead of just describing it. “Here are three emails we’ve sent before. Write one like these for this new product.” The AI picks up on patterns in your examples and mirrors them. Chain of thought prompting is when you ask the AI to think step by step before giving a final answer - useful when the task requires reasoning.
None of these require a technical team. A business analyst can learn prompt engineering in a few weeks and start getting real value out of it. If there’s one thing from this entire post that you should act on immediately, it’s this.
RAG
Now we get into the slightly more technical stuff.
Remember when I said LLMs don’t actually know things? RAG - Retrieval Augmented Generation - is a way to deal with that problem. The idea is simple. Before the AI generates a response, you first retrieve relevant information from your own data and feed it to the AI along with the question.
Let me give you a concrete example.
Say you have a customer support chatbot for your insurance company. A customer asks “What does my policy cover for water damage?” A plain LLM would either make something up or give a generic answer about water damage policies. Neither is acceptable.
With RAG, the system first searches your actual policy documents, finds the relevant sections about water damage coverage, and then gives those sections to the LLM along with the customer’s question. Now the LLM can generate a response that is grounded in your actual data. It’s still generating text, but it’s generating text based on real information that you provided.
This is arguably the most practical AI pattern for businesses right now. Your company has data. A lot of it. Policy documents, product manuals, internal wikis, support tickets, contracts, regulatory guidelines. This data is sitting in various systems and nobody reads all of it. RAG lets you make that data accessible through natural language. An employee can ask “What’s our return policy for enterprise clients in the EU?” and get an actual answer pulled from the actual policy document, not a hallucinated one.
To build a RAG system you need a few moving parts - a vector database to store your documents in a searchable format, an embedding model to convert text into that format, and an LLM to generate the final response. There’s also the question of how you split your documents into chunks, which matters more than you’d think. But these are implementation details. The important thing to understand is the pattern: retrieve first, then generate.
RAG is not magic. It works well when implemented thoughtfully and falls apart when implemented carelessly. But for most business use cases, it’s the right starting point.
Fine-Tuning
If prompt engineering is talking to the AI better, and RAG is giving the AI better information, fine-tuning is changing the AI itself.
When you fine-tune an LLM, you take an existing model and train it further on your own data. The model learns your specific patterns, your terminology, your style, your domain.
When does this make sense? Honestly, less often than people think.
Fine-tuning is expensive, time-consuming, and requires technical expertise. You need quality training data, which means someone has to curate and label it. You need compute resources. And then you need to evaluate whether the fine-tuned model actually performs better than a good prompt with RAG.
It makes sense when your brand voice is very specific and prompt engineering isn’t nailing it after serious effort. It makes sense in specialized domains - medical, legal, financial - where the base model keeps getting terminology wrong. It makes sense when you need a smaller, faster model for a very specific task that your company runs thousands of times a day. But for most businesses just starting with AI, prompt engineering plus RAG will get you 80% of the way there. Fine-tuning is for the remaining 20% when you’ve already exhausted the simpler approaches.
A word of caution. Fine-tuning can also make models worse. If your training data has biases, the model learns those biases. If your data is low quality, the model’s output quality drops. Garbage in, garbage out. This is not a “more is better” situation.
AI Agents
We’ve covered the building blocks. Now let’s talk about putting them together.
AI agents are systems that don’t just respond to a single question. They can take a goal, break it down into steps, use tools, and work through the steps with some degree of autonomy. Think of the difference between asking someone a question and giving someone a task.
A chatbot answers your question. An agent books your flight, checks your calendar, sends the confirmation to your assistant, and adds the trip to your expense tracker.
Where are agents today? The honest answer is early.
Agents work well for structured tasks with clear boundaries. Customer service workflows, data processing pipelines, scheduling - things where the steps are somewhat predictable and the cost of mistakes is manageable. They struggle with open-ended tasks where the path isn’t clear. They struggle when they need to make judgment calls. And they struggle when the cost of getting it wrong is high.
If an agent books the wrong flight, that’s fixable. If an agent sends the wrong contract to a client, that’s a different conversation entirely.
The companies getting the most value from agents right now are using them for internal workflows where a human reviews the output before it goes anywhere consequential. The fully autonomous agent that runs your business while you sleep? That’s not here yet. And the people telling you it is are selling something.
Guardrails
This one doesn’t get enough attention.
When you deploy AI in a business context, you need guardrails. These are rules and systems that constrain what the AI can do and say.
You need output validation - checking the AI’s response before it reaches the user. Does it contain sensitive information? Does it make promises your company can’t keep? Does it stay within the scope of what it should be answering?
You need input filtering - preventing users from manipulating the AI into doing things it shouldn’t. People will try to trick your chatbot. It’s not a matter of if, it’s when. Remember the Chevrolet dealership chatbot that agreed to sell a car for one dollar? Or the early Bing Chat that could be convinced it was named Sydney and would profess its love to users? Someone will try this with your system. Probably on day one.
And for high-stakes decisions, you need a human in the loop - a step where a person reviews the AI’s work before it’s acted on. This is not optional for anything involving money, legal commitments, or customer-facing communication at scale.
Guardrails are not glamorous. They don’t make for good demos. But they are the difference between a toy project and something you can actually deploy in production.
A quick side note on cost
Nobody talks about this enough, but AI is not cheap at scale.
API calls cost money. Every time a user asks your chatbot a question, you’re paying for tokens. A few cents per query doesn’t sound like much until you have ten thousand customers using it daily. And if you’re doing RAG, you’re also paying for embedding calls and vector database hosting.
The costs are manageable, but they’re not zero. And they scale with usage in ways that traditional software doesn’t. This isn’t a reason to avoid AI - it’s a reason to measure your costs from day one and understand what you’re paying for. I’ve seen companies get surprised by five-figure monthly bills because nobody was tracking usage during the pilot phase.
Other things worth knowing
Modern AI models can work with more than just text. They can process images, audio, video, and documents. Insurance claims processing where the AI looks at photos of damage. Quality control where the AI inspects product images on a manufacturing line. Meeting transcription and summarization from audio. Document processing where the AI reads scanned PDFs. If your business deals with unstructured media, this is worth looking into. The field moves fast. But the fundamentals in this post - prompting, retrieval, fine-tuning, guardrails - those are stable.
So where do you start?
If you’ve made it this far, you might be wondering what to actually do with all of this.
Start with prompt engineering. Pick a real business problem - drafting emails, summarizing reports, analyzing feedback - and spend time crafting good prompts. This costs almost nothing and gives you a feel for what AI can and cannot do.
Then identify your data. What internal information would be most valuable if it were easily searchable? That’s your RAG candidate. Your internal knowledge base, your product documentation, your support ticket history.
Build a proof of concept. Use an off-the-shelf RAG solution to connect an LLM to your data. There are plenty of tools that make this accessible without a full engineering team. Test it internally before you put it in front of customers.
Measure ruthlessly. AI demos are always impressive. Production AI is a different beast. Measure accuracy, measure user satisfaction, measure the cases where it fails. The failures will teach you more than the successes.
Only then consider fine-tuning or agents. These are powerful tools, but they add complexity. Reach for them when the simpler approaches aren’t enough.
The real risk
Every vendor will tell you their AI solution is going to transform your business. Most of them are telling you what you want to hear.
AI is a tool. A very powerful one, yes. But still a tool. It works best when applied to well-defined problems with clear success criteria and enough data to make the results reliable. It works worst when it’s deployed because someone read an article about it, with no clear problem to solve and no way to measure whether it’s working.
Here’s what I think the real risk is. It’s not that AI won’t work. It’s that companies will spend millions on AI initiatives that solve no real problem, declare AI a failure, and miss the actual opportunities. I’ve already seen this happen. A flashy chatbot gets built, nobody uses it because it doesn’t solve a real pain point, and leadership concludes that “AI isn’t ready for us.”
The businesses that will get the most out of AI are not the ones that adopt it the fastest. They are the ones that adopt it the most thoughtfully. That means understanding what these tools actually do, where they excel, where they fail, and having the discipline to start simple.
The jargon will keep evolving. But the fundamentals - give the AI good context, ground it in real data, validate its output, keep humans in the loop for what matters - those aren’t going anywhere.
Start there.