Understanding roughly how AI code generators work — without needing a machine learning background — makes their strengths and limitations far less mysterious. This guide covers the basics of large language models and what “training on code” actually means.
If you’ve worked through our guides on GitHub Copilot and whether AI replaces learning to code, this guide fills in the “how” behind those tools — useful context for understanding both their genuine strengths and their real limitations.
The Core Technology: Large Language Models
According to AWS’s explanation of large language models, LLMs are proficient at code generation from natural language prompts, built on transformer-based neural networks — very large systems of interconnected nodes that process and generate text (and code) by predicting what comes next based on patterns learned during training.
What “Training on Code” Actually Means
According to the GitHub Blog’s explanation of AI code generation, the models powering tools like ChatGPT and GitHub Copilot are trained on natural language text and source code from publicly available sources spanning a diverse range of examples — enabling them to understand the nuances of various programming languages, coding styles, and common practices.
In practice, this means the model has seen an enormous number of real code examples during training and learned statistical patterns about what tends to follow what — which is why it’s excellent at common, frequently-seen patterns (a REST API handler, a sorting algorithm) and less reliable on unusual, highly specific, or proprietary code it’s less likely to have encountered anything similar to.
How a Suggestion Actually Gets Generated
When you’re typing in an editor with an AI assistant active, the tool gathers context — your current file, nearby comments, sometimes other open tabs — and sends that as a prompt to the underlying model. The model predicts a likely continuation based on everything it learned during training, and the editor displays that prediction as a suggestion you can accept, modify, or ignore.
Why Context Windows Matter
AI coding tools don’t automatically see your entire codebase — they work from a limited “context window,” typically your current file and whatever else is explicitly provided. This is a genuinely important limitation: a suggestion might miss important logic or conventions defined in a different file that simply wasn’t included in what the model could see for that particular request, which is exactly why the specificity and context-providing habits covered in our earlier guides matter so much in practice.
Why These Tools Sometimes “Hallucinate”
Because a model generates code by predicting statistically likely patterns rather than verifying facts, it can occasionally produce code that references a function or library that doesn’t actually exist — confidently and convincingly, since the model is optimizing for “what looks like a plausible continuation,” not “what is guaranteed to be real.” This is precisely why testing every suggestion, covered throughout this series, isn’t optional caution — it’s a direct consequence of how these tools fundamentally work.
How Suggestions Actually Get Ranked
Modern AI coding tools don’t just generate one possible continuation — internally, the model typically produces several candidate completions and ranks them by predicted likelihood before showing you the top result. According to a detailed breakdown of GitHub Copilot’s architecture, the plugin in your editor monitors what you’re typing in real time and decides when to trigger a completion request, sending relevant context — like the current file’s content — to the underlying model as part of that request.
Fine-Tuning: Teaching a General Model to Write Code Well
Many coding-focused AI tools start from a general-purpose language model and then go through additional training specifically on code — a process called fine-tuning. This extra step is part of why coding assistants tend to outperform general chat models on programming-specific tasks: the underlying model has been further shaped by exposure to large amounts of real code, syntax patterns, and common programming idioms beyond its original general-purpose training.
Autocomplete vs. Chat: Two Different Interaction Modes
AI coding tools generally operate in one of two modes, which rely on the same underlying model but interact with it differently. Inline autocomplete (like Copilot’s “ghost text”) continuously predicts what comes next as you type, optimized for speed and minimal interruption. Chat-based interaction (like Copilot Chat or a general chatbot) is a back-and-forth conversation, better suited to open-ended questions, explanations, and multi-step reasoning. Understanding which mode you’re in helps set the right expectations — autocomplete is genuinely fast for routine code, while chat is better for anything requiring actual discussion or clarification.
Why Recent Language Features Sometimes Trip Up AI Tools
Since models learn from training data collected up to a certain point, and that data reflects however common a given pattern was at the time, genuinely new language features, libraries, or frameworks — released after a model’s training cutoff, or simply less common in the code it learned from — are areas where AI suggestions are noticeably less reliable. This is a direct, predictable consequence of how training data works, not a random or unexplainable weakness, and it’s exactly why checking official documentation remains valuable even when an AI tool’s suggestion looks confident.
Common Questions
Does a bigger model always produce better code? Generally, larger, more capable models tend to perform better on complex reasoning tasks, but size alone doesn’t guarantee correctness — the fundamental behavior of predicting plausible patterns, and the resulting need for verification, applies across models of every size.
Why does Copilot sometimes miss context from other files? By default, it primarily works from what’s currently open in your editor, not your entire project — a known limitation in larger, multi-file projects that’s improving over time as tools add broader project awareness across an entire codebase rather than just the active file.
Do these tools get better automatically over time? The underlying models are periodically retrained or replaced with newer versions, generally improving capability, but any specific installed tool doesn’t silently get smarter on its own between official updates — improvement happens through new model releases, not gradual self-learning from your usage.
The Role of Human Feedback
Beyond training on raw code, many modern AI models go through an additional stage involving human feedback — people rating or ranking model outputs to help steer the model toward responses that are actually more helpful, not just statistically plausible. This process, sometimes abbreviated RLHF (reinforcement learning from human feedback), is part of why newer AI tools generally feel more genuinely useful and better-calibrated than earlier generations, beyond just having seen more training data. It’s a meaningful part of the picture, even though the core mechanism — predicting likely continuations based on learned patterns — remains fundamentally the same.
Quick-Reference Guide
- LLM — a large language model, trained on massive amounts of text and code.
- Training data — public code examples the model learned patterns from.
- Context window — what the model can actually see when generating a suggestion.
- Prediction, not verification — models predict plausible code, they don’t guarantee correctness.
- Hallucination — confidently generating something that doesn’t actually exist.
Conclusion
AI code generators work by predicting statistically likely code based on massive amounts of training data — genuinely powerful for common patterns, genuinely limited by context windows and the inherent uncertainty of prediction rather than verification. Understanding this mechanism explains exactly why the verification habits covered throughout this series matter as much as they do.
In the next guide in this series, we’ll cover using Claude specifically for coding tasks, closing out the tool-specific guides in this AI-Assisted Coding series.
Explore More AI-Assisted Coding Guides →
When not writing, Alex is probably debugging someone else’s code.