Getting Started With AI / AI Foundations
Neural Networks, LLMs and SLMs
A simple explanation of neural networks, language models, large language models, and small language models.
A simple explanation of neural networks, language models, large language models, and small language models.
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Executive note
The Core Idea
A neural network is a machine learning model inspired by the idea of connected processing units. It learns patterns by adjusting internal weights during training.
Modern AI uses neural networks for many tasks: image recognition, speech processing, forecasting, translation, summarization, code generation, and chat.
A language model is a neural network trained to understand and generate text. It learns statistical patterns in language and uses them to predict useful continuations.
Section 2 of 5
What Is an LLM?
A large language model, or LLM, is a language model trained at large scale. It usually has broad language ability, strong instruction following, and enough general knowledge to help with many tasks.
LLMs are useful for:
The trade-off is that large models can be slower, more expensive, and harder to operate in constrained environments.
- Complex reasoning over messy context.
- Drafting and rewriting.
- Summarizing long material.
- Explaining technical concepts.
- Generating code or tests.
- Coordinating multi-step workflows.
Section 3 of 5
What Is an SLM?
A small language model, or SLM, is a smaller model designed for narrower tasks, lower cost, lower latency, or deployment closer to the workload.
SLMs can work well for:
An SLM is not automatically less valuable. If the task is narrow and well-scoped, a smaller model may be the better engineering choice.
- Classification.
- Extraction.
- Routing.
- Short rewriting.
- Domain-specific assistants.
- High-volume tasks where speed matters.
Section 4 of 5
Model Routing
Model routing means sending different tasks to different models.
A practical pattern is:
This avoids using the largest model for everything and helps control cost, latency, and risk.
- Use a small model for simple, frequent, low-risk tasks.
- Use a larger model when ambiguity, reasoning, or synthesis increases.
- Escalate to a human when impact or uncertainty is high.
Section 5 of 5
Remember This
Do not choose a model by size alone. Choose it by task quality, latency, cost, privacy needs, deployment constraints, reliability, and how easily the result can be evaluated.