From understanding the basics to building your first project, step by step with a focus on real-world applications.
AI Fundamentals4 min read
What Is AI, and What Can It Do for Your Business?
From customer support to data analysis, explore the practical uses and real limitations of AI.
AI in Plain English
Artificial intelligence describes systems that use data to recognize patterns and perform tasks such as classification, prediction or text generation. A language model generates responses from learned patterns, so a fluent answer is not necessarily a correct one.
Where Should You Start?
Choose one clearly defined, repetitive task: sorting support requests, drafting emails or summarizing documents. Measure how long the task takes and the current quality of the results. Then build a small prototype and compare it with your existing process. The goal is to solve a real problem, not simply adopt new technology.
Important Limitations
Model outputs can be incorrect, outdated or biased. Legal, medical, financial and other sensitive decisions about people should be reviewed by qualified professionals. Do not enter confidential information into public tools without checking their data retention terms and obtaining your organization's approval.
Practical Guide3 min read
How to Write Better AI Prompts
A simple framework for writing clear requests and getting useful responses.
Goal, Context and Format
Specify the output you need, who the audience is and which information the response should rely on. Set the length, tone and format. Provide an example of a good result and ask the model not to guess missing information.
An Example Prompt
"Using the product description below, write an English email of no more than 120 words to a company's purchasing manager. Use a professional tone, mention three benefits supported by the description and end with an invitation to a meeting. Do not invent prices or features. If information is missing, ask a question." Then add your actual product description.
Review and Refine the Response
Check names, numbers, claims and sources against the original information. If the result is not suitable, explain the specific issue, such as: "Make it shorter and remove technical jargon." For recurring tasks, keep a fixed set of test examples so you can compare the quality of new prompts.
Getting Started5 min read
A Roadmap for Your First AI Project
From choosing a problem and preparing data to testing, budgeting and launching responsibly.
1. Define the Problem and Success Measures
Define a focused problem and assign an owner. For example, reduce the time it takes to find answers in internal documents. Before building, set measures such as answer accuracy, time saved, cost per request and the proportion of requests referred to a person.
2. Prepare Data and Build a Prototype
Gather relevant, up-to-date data that you have permission to use, and remove unnecessary personal information. Retrieval-augmented generation (RAG) can help answer questions using company documents. This often reduces the need to retrain a model, but it does not eliminate errors.
3. Evaluate Quality and Safety
Test the prototype with real questions, ambiguous cases and inappropriate requests. Respect each user's document access permissions. Do not treat text inside documents as trusted instructions. For actions such as sending messages, making payments or changing records, require human approval and limit access.
4. Launch Gradually
Start with a small group of users, collect feedback and monitor quality and cost. Keep a way to return to your previous process and provide human support. Your budget should cover more than the model: data preparation, tool integrations, maintenance and output reviews also contribute to the project's cost.
