Llm

For most of my career, integrating external intelligence into an application meant calling a rules engine, training a custom classifier, or encoding business logic that someone had painfully documented in a spreadsheet. The idea that I could describe a task in plain language and have a model respond with genuine reasoning was not something I expected to become production-ready in my working life. Then GPT happened, and it changed what backend developers need to know. This article is the first in a series on using LLMs in Go. We start with the OpenAI Chat Completions API — the stateless, request-based interface that gives you direct control over every aspect of the conversation. By the end, you will have a working conversational agent that can call external tools to answer questions it otherwise could not. A short introduction to ChatGPT # The path to large language models runs through a decade of incremental progress in deep learning. Early models like word2vec and GloVe learned to embed words into dense vector spaces, capturing semantic relationships between terms. The transformer architecture, introduced by Google in 2017, changed the trajectory of the field — it processes sequences in parallel using attention mechanisms that capture long-range dependencies far more effectively than recurrent networks. This architectural shift made it practical to train models on orders of magnitude more data. GPT-1 in 2018 showed that large-scale unsupervised pre-training followed by fine-tuning could match or beat purpose-built models across a range of language tasks. Understanding what these models actually do removes a lot of the mysticism around them. An LLM is, at its core, a next-token predictor. It takes a sequence of tokens as input and outputs a probability distribution over the vocabulary for the next token. The transformer’s attention mechanism allows every token in the input to attend to every other token, building a rich contextual representation before making that prediction. Training adjusts billions of parameters to minimise prediction error across enormous text corpora. What emerges is a model with broad world knowledge encoded in its weights — not because it was taught facts directly, but because predicting text well requires internalising the structure of the world that produced that text. ChatGPT is OpenAI’s conversational product built on the GPT model series. What set it apart from raw GPT-3 was the addition of reinforcement learning from human feedback (RLHF) — a technique that fine-tunes the base model to follow instructions and produce responses that human raters judge as helpful and safe. When ChatGPT launched in late 2022, it became one of the fastest-adopted consumer products in history. For developers, the more relevant artefact is the API behind it — specifically the Chat Completions API, which gives programmatic access to the same models powering the product.