You do not need to learn every AI term before deciding where it belongs in your business. Start with something a customer needs, then follow the work required to deliver it. The terms become easier when each has a clear job.
Imagine a customer asks a shop: “Do you have this lamp in blue, and can I collect it tomorrow?” This is an illustrative example, not a Looqus customer story. The analogies below are learning aids, not descriptions of how a model thinks. A helpful answer needs more than good writing. It needs the exact lamp, current stock at the right branch, time to prepare it and someone who can confirm what will happen.
The model works with the question
A large language model, or LLM, can help interpret the customer's message. It might identify the product, colour and requested collection time. A prompt tells it what to do with that information: check the required facts, avoid guessing and prepare a clear answer.
The model needs context: the information available while it works. In this case, that might include a product record, the collection policy and the customer's original message. Context is not the same as everything the company has ever known. It is the relevant material supplied for this particular job.
Text is processed in pieces called tokens. You do not need to count them by hand, but they help explain why an AI system cannot always use an unlimited pile of documents at once. Supplying the useful pages is usually a better starting point than supplying every page. Hugging Face: How tokenisers process text.
The system finds evidence instead of guessing
The approved product record can establish which lamp the customer means. Retrieval is the step that finds relevant information. RAG means supplying that retrieved information to the model before it generates an answer. It is useful when the answer depends on your business's records rather than general knowledge. Microsoft: Retrieval-augmented generation.
Some search systems use embeddings to find related information even when the wording differs. A question about “a blue bedside light” may lead to a record named “Navy table lamp.” That gives the system a candidate, not proof that it found the right product. It still needs to check the model, colour and other relevant details.
Current stock requires another step. Last month's brochure cannot show what remains in the branch now or whether it can be ready tomorrow. The system should ask the inventory source and keep the result tied to the exact item, location and time checked. If that connection fails, it should say what is missing and give the unresolved question to an owner.
Connections provide access, not completion
An API is one way software can ask another system for information or an action. In our example, a stock-check tool could request the quantity of the blue lamp at the chosen branch. The tool returns a result that the application can use.
MCP is a standard way for AI applications to connect to tools and information. It can make those connections more consistent. It does not establish whether the stock count is right, whether the employee may reserve the item or whether anyone has prepared it. Model Context Protocol: Introduction.
That is why connecting your apps is the beginning of an implementation. A working connection still needs the correct records, clear rules, permitted actions, failure handling and evidence that the job finished. “The systems are connected” is useful progress. The customer is waiting for an accurate collection answer.
A workflow follows the plan; an agent can choose some steps
A workflow could follow a known sequence: identify the lamp, check stock, ask staff to prepare it, then send the approved readiness message. A simple automation can run a rule when a defined event occurs, without using AI at all.
An agent can choose some of its next steps within the tools and limits it has been given. If the branch has no lamp, it might check another approved location or ask an authorised person about an alternative. The workflow-versus-agent distinction is useful, although product labels vary. Anthropic: Workflows and agents.
More freedom is not always better. If the task has a reliable fixed path, a simple workflow may be easier to test and operate. If it requires investigation, limited agent choices may help. Neither should invent a discount, substitute another product or promise a transfer outside the business's authority.
A confident answer still needs checking
A hallucination is an incorrect or unsupported generated claim. In the lamp example, it could be an invented collection time or warranty. The answer may sound natural, which is why tone is a poor test of accuracy.
Evaluation checks the system on defined examples. Include the easy case, the last item, missing stock data, a similar product and a request the employee is not allowed to approve. The expected result is sometimes a hold or a request for help. A system that knows where its evidence ends can be more useful than one that always produces a complete-looking answer.
Fine-tuning is different from giving the model today's facts. It adds training to adapt model behaviour for a task. It may help with a specialised pattern, but it does not replace the live stock check. Decide whether the problem is behaviour, missing information or a broken process before adding another layer of technology.
Finish with the customer outcome
Suppose the system reports one lamp, but the stockroom team finds it damaged. The AI has not completed the job by producing a stock result. Someone must resolve the problem: identify an acceptable option, obtain the necessary decision and tell the customer clearly.
This is where ownership matters. The specialist's role is to close missing facts, coordinate people and verify the next action. The finish might be the correct lamp prepared and collected. It might be an honest explanation that the shop cannot supply it by the requested time. Both are clearer outcomes than an unanswered exception hidden behind a successful tool call.
For Looqus, AI does the work. We own the outcome. The agreed scope should identify what completion means, which decisions remain with your authorised team and who resolves the work that stalls. It should also include the cost of that continuing responsibility.
When comparing proposals, ask what the customer or employee will be able to complete, what evidence proves it, and what happens when the usual path fails. Those questions are useful whether the solution uses a simple rule, a workflow or an agent.
Explore the concepts above in any order. Each connects a plain explanation to a business example, a limitation and a question worth asking before you buy. When you are ready to discuss a specific piece of work, book a call with Looqus.
