Most business problems don’t appear suddenly — they build up gradually in data that nobody is watching closely enough. A customer churn rate that’s been creeping up for six weeks. A fulfilment error rate that doubled two months ago and nobody noticed. An ad campaign whose cost-per-click quietly tripled while everyone was focused elsewhere. Anomaly … Read more
Fine-tuning is only half the work. The other half — the part most teams skip or rush — is figuring out whether the model you just trained is actually good enough to deploy. Running a training job and seeing a low loss number isn’t sufficient. The loss metric tells you the model learned your training … Read more
You’ve probably noticed that ChatGPT and Claude feel different from raw language model outputs. They’re helpful rather than just predictive. They refuse certain requests. They acknowledge uncertainty. They follow instructions rather than just continuing text patterns. None of that came from the original pre-training — it came from a process called RLHF. RLHF stands for … Read more
There’s a common tension in deploying AI in production: the models that perform best are large and expensive, but the models that are fast and cheap often don’t perform well enough. Model distillation is a technique that tries to get the best of both — taking the performance advantages of a large model and transferring … Read more
One of the more counterintuitive ideas in modern AI development is this: you can use an AI to generate the training data that trains a different (or better) AI. It sounds circular, but it works — and for businesses trying to fine-tune models for specific tasks, it solves one of the most practical problems in … Read more
Fine-tuning a large AI model used to mean one thing: pay a lot of money, wait a long time, and hope it works. Training a model like GPT-3 from scratch costs millions of dollars. Even fine-tuning the full weights of a large model requires serious GPU hardware and significant compute time. LoRA — Low-Rank Adaptation … Read more
When you use a tool like ChatGPT or Claude through a browser, your text travels to a data centre, gets processed by a massive model, and the response comes back to you. That’s cloud AI — and it works brilliantly for most things. Start with the workflow that scores highest on the four-point framework above … Read more
If you’ve heard of Hugging Face but assumed it was only for machine learning researchers, you’re not alone — and you’re wrong. Hugging Face has quietly become the most practical resource on the internet for any business that wants to use, evaluate, or customise open-source AI models. You don’t need a data science team to … Read more
You may have seen the term “mixture of experts” applied to models like GPT-4, Mistral’s Mixtral, and several other leading AI systems. It sounds technical, but the core idea is straightforward — and understanding it helps you make sense of why some AI models that appear equally capable on benchmarks are dramatically different in cost … Read more
The AI model you depend on today will eventually be deprecated. OpenAI has already retired GPT-3.5-turbo-0301, text-davinci-003, and multiple other model versions. Anthropic has deprecated Claude 1 and Claude 2. Google has retired PaLM models. This is the normal lifecycle of AI models — providers retire older versions as better ones become available, as compute … Read more