An LLM Is Not Your Backend: Lessons Learned

When I first started working with artificial intelligence, I used to think of a large language model as something like a super-smart backend. You provide input, it understands, processes, and outputs an answer, which led me to question why extensive backend logic was necessary when I could simply instruct the LLM on application functions and let it handle everything.

It turns out that is not the case, and grasping this realization fundamentally shifted my perspective on building AI applications.

In simple terms, an LLM is a system trained on massive amounts of text that learns linguistic patterns and utilizes those patterns to generate text. Imagine spending a lifetime reading books, articles, conversations, documentation, stories, emails, and code until you become exceptionally skilled at predicting what sentence structure makes sense next, such as anticipating the word east when someone mentions the sun rises in the.

Stock photo for illustration only, not from the actual event

An LLM performs something conceptually similar while leveraging mathematical representations and neural networks at scale. This explains why they accomplish diverse tasks like explaining concepts, writing code, summarizing documents, translating languages, generating stories, analyzing text, and conversing, making it feel as though you are communicating with an entity that understands everything underneath.

Let us consider a simple example. If I state that I went to the restaurant and ordered, your brain anticipates words like food, pizza, dinner, or burger. An LLM calculates token probabilities mathematically based on context, such as 32% for pizza, 21% for food, 15% for dinner, and so forth, illustrating that the model continuously predicts subsequent elements from received context.

Why then does it feel cognitive? When asking an LLM to explain recursion to a five-year-old, write Java solutions, rewrite professional emails, or summarize extensive documents, it reasons through approaches and adjusts tones convincingly, simulating an actual person behind the screen performing sophisticated problem-solving.

"Sure! Your pizza has been ordered."

LLM

Imagine building an online food delivery application featuring an AI assistant. You transmit a message, and the LLM responds that your pizza has been ordered. That sounds wonderful, but did it actually order anything? No, the model merely generated a sentence without verifying open restaurants, item availability, user addresses, payment processing, database order creation, or restaurant dispatching.

This distinction became stark when I began evaluating AI applications as actual software products rather than mere chatbots. LLMs excel at understanding natural language, extracting intent, generating responses, summarizing information, handling context, and selecting tools.

Source: Dev.to