How Do AI Models Learn and Improve?
In the last section, we introduced the big-picture building blocks of modern AI. Now let’s look at how these models actually learn and evolve. At Gloo AI, we often describe this as the model’s life cycle from raw data to a working system that continues to improve over time. These concepts explain what happens before, during, and after a model is trained, and how organizations fine-tune them to be safer, more helpful, or more domain-specific.Pre-training
What it means: Pre-training is the first major phase of model development. It’s when a model is fed massive amounts of data (like books, websites, and code) so it can learn general patterns in language, images, or other types of input. What to know: This process doesn’t teach the model facts. It teaches it how language or imagery behaves so it can later predict and generate content. How it shows up in Gloo: While Gloo hasn’t launched any pre-trained models to date, keep an eye out for news about Project Genesis!Post-training
What it means: Post-training includes all the steps that happen after pre-training like refining the model for safety, quality, or specific use cases. Why it matters: Post-training turns a general-purpose model into a useful product. It might involve adding safety rules, formatting guidelines, or behavior alignment.Fine-tuning
What it means: Fine-tuning is when a pre-trained model is retrained on a smaller, curated dataset to specialize in a topic, task, or tone. Use case: If you wanted a model to work only with legal documents or theological texts, you’d fine-tune it on relevant examples to get better performance in that domain.Instruction Tuning
What it means: Instruction tuning teaches the model how to follow human instructions more reliably. It’s done by showing examples of clear instructions and good responses. Why it helps: This is what makes models like ChatGPT feel responsive and helpful. It teaches models not just to predict, but to follow your intent.Reinforcement Learning from Human Feedback (RLHF)
What it means: RLHF is a method where humans rate different model outputs, and the model learns to prioritize responses that align better with human preferences. Analogy: It’s like giving the model a thumbs up or down for each answer, and letting it learn what types of replies people actually want to see.Alignment
What it means: Alignment is the process of making sure a model’s behavior stays consistent with human values, safety guidelines, and ethical boundaries. Why it matters: A powerful model that isn’t aligned could give unsafe, biased, or manipulative responses. Alignment helps ensure models behave as intended even in unexpected situations. How it shows up in Gloo: Alignment is central to Gloo’s approach. Gloo applies custom system instructions, safety layers, theological constraints, and allowed content boundaries to ensure responses reflect the values of each organization and never contradict their content. The Data Engine and Studio ensure answers are grounded in approved material.Distillation
What it means: Distillation is the process of compressing a large model into a smaller, faster version while keeping most of the same abilities. Use case: Companies often use distillation to deploy smaller versions of powerful models on phones, websites, or embedded devices where resources are limited.Transfer Learning
What it means: Transfer learning means applying knowledge learned in one context to another. A model trained on general language can adapt to specialized tasks without starting from scratch. Example: You could take a model trained on internet text and quickly adapt it to answer medical questions without retraining the whole system. How it shows up in Gloo: Gloo uses retrieval instead of training the model on new data. By combining a pre-trained LLM with your uploaded content via the Data Engine, Gloo achieves the benefits of transfer learning without modifying the model. This means faster updates, safer usage, and no risk of leaking proprietary content.Next Up: How We Talk to Models In the next section, we’ll answer: “How do we give AI models instructions, and how do they turn those prompts into useful answers?”