AI 101

In today's AI-driven world, almost every business is enhancing its applications with artificial intelligence. If you want to strengthen your operations with AI but aren't sure how it works, this post will help you understand the basics—even if you're not technically minded.

Ways to Integrate AI

When it comes to integrating AI into your application, there are two options. The first is building your own AI model, which requires a large amount of data and significant CPU computation. While training your own model can be rewarding but, it comes with a high cost. Fortunately, there's a second option: using pre-trained models like ChatGPT, Gemini, Stable Diffusion, and others—something 90% of people opt for.

Understand Pricing

Tokens

When purchasing AI services from a provider, you're typically charged based on the number of tokens used. But what is a token? A token is a unit of text used in natural language processing. It's not exactly a character or word, but rather a chunk of text with no fixed length. According to OpenAI, a token is usually 3 to 4 characters long. Tokens length also varies depending on the AI model you're using. To better understand this, you can try OpenAI's playground, which offers a hands-on experience with token usage.

Context Window

In the pricing section, most providers also mention the model's context window. A context window refers to the amount of text or tokens the AI model can process at once. The larger the context window, the more information the model can consider, which improves its ability to handle longer conversations effectively.

Fine-tuning

Most AI providers allow you to fine-tune the model according to your specific needs, enabling it to respond based on your data. Fine-tuning requires a high-quality dataset and can reduce token costs and latency. However, it can be expensive and is often unnecessary for small use cases.

Price optimization

Batch APIs

Most AI providers offer batch APIs that provide a 50% discount on standard pricing. Rather than delivering instant results, these APIs promise to return responses within 24 hours. This approach works well for background tasks where immediate responses aren't critical, such as processing customer reviews.

Caching

Caching is a technique used to store computed values and return them when requested again, without needing to recompute them. While not unique to AI, caching is a well-established practice in computer science. Some AI providers leverage this technique to cache user prompts, which reduces latency and lowers the number of input tokens required for repeated queries.