Prompt, RAG, fine-tune: three ways to shape a model
Three levers for shaping what an LLM does: prompting (ask better), RAG (give it the right context), fine-tuning (change the weights). What each costs, what each fixes, and how to pick.
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Posts on AI engineering, LLM systems, and software development.
RAG: giving a model memory it doesn't have
RAG is the pattern of fetching relevant text from a search system and putting it in the LLM's context window before asking your question. Not magic, not fine-tuning — just better prompts.
The context window, and why models hallucinate
An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway — that's a hallucination, not a bug.
From models to LLMs
An LLM is one kind of ML model — trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.
How a model learns: training and inference
Training is the expensive one-time event where a model's numbers get tuned. Inference is the cheap repeated use afterwards. The gap in cost is enormous, and it shapes the whole industry.
What makes a model: data and algorithm
A model is a file of learned numbers, produced by running an algorithm over data. Both ingredients matter, but bad data beats a good algorithm every time.
Inside AI: machine learning and deep learning
Open the AI umbrella. Machine learning is the part that learns from data. Deep learning is ML done with neural networks — and that's where today's models live.