Building With LLM: A Practical System
A model trained on huge text corpora that predicts the next token to produce human-like language. A Large Language Model is a neural network — usually a Transformer — trained to predict the next token given a context. This guide pulls together everything on Onexial tagged llm — 12 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind LLM
Before wiring anything together, the vocabulary has to be precise. These 7 definitions cover the terms that show up in almost every LLM discussion — each one links to a full entry with an example and its own connections inside the graph.
LLM (Large Language Model)
A model trained on huge text corpora that predicts the next token to produce human-like language.
Small Language Model (SLM)
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
Local LLM
A language model run on your own machine or server instead of a cloud API, so prompts and data never leave your infrastructure.
Reasoning Model
An LLM trained to spend extra compute thinking through a problem internally before answering, improving accuracy on math, code and planning.
Contextual Compression
Contextual compression is a technique used to reduce the size of the input context for a Large Language Model (LLM) while retaining its most relevant information, typically by summarizing or filtering.
Token Budgeting
Token budgeting is the strategic allocation and management of token usage within large language model (LLM) operations to control costs and optimize performance.
AI Search Ranking Factors
AI search ranking factors are the criteria and signals used by generative AI models to determine the relevance, quality, and trustworthiness of information when formulating responses or generating content.
Workflows: how LLM runs end to end
Concepts only matter once they become a repeatable process. Below are 2 documented workflows that apply LLM to a concrete problem, with the steps, the tools involved and the variations worth testing.
Run an LLM Locally
Set up a private, offline language model on your own computer in under an hour.
Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
The LLM tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 1 stacks show which combinations hold up in production for LLM, and what each layer is actually responsible for.
Trade-offs and comparisons
Most LLM decisions are trade-offs rather than right answers. These 2 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
Local LLM vs Cloud API
Local models win on privacy and fixed cost; cloud APIs win on quality, scale and zero maintenance.
Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.
Frequently asked questions
- What is LLM (Large Language Model)?
- A Large Language Model is a neural network — usually a Transformer — trained to predict the next token given a context. Modern LLMs (GPT, Claude, Gemini, Llama) can reason, write code, follow instructions and call tools when wrapped in the right runtime.
- What is an example of LLM (Large Language Model)?
- You send a prompt like 'summarize this contract in 5 bullets'. The LLM tokenizes the input, runs it through billions of parameters and streams tokens back until a stop condition.
- Why does LLM (Large Language Model) matter?
- A model trained on huge text corpora that predicts the next token to produce human-like language.
- What should I learn after LLM (Large Language Model)?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
- What is Small Language Model (SLM)?
- A small language model trades broad world knowledge for low latency, low cost and the ability to run privately on local hardware. SLMs are strongest on bounded tasks — classification, extraction, routing, short summaries — especially after fine-tuning on domain data. They are weaker at long multi-step reasoning and open-ended knowledge questions.
- What is an example of Small Language Model (SLM)?
- A support team runs a 3B model on-premise to tag every incoming ticket by intent and urgency, and only sends the hard 10% to a frontier model.
- Why does Small Language Model (SLM) matter?
- A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
- What should I learn after Small Language Model (SLM)?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.