What You Will Learn

In this article, you will learn:

• What an LLM is.
• How LLMs are trained and used.
• What prompting and inference mean.
• Common LLM application patterns.
• Practical limitations and risks.

Introduction

A Large Language Model, or LLM, is a model trained to understand and generate language.

LLMs can:

• Answer questions.
• Summarize documents.
• Generate code.
• Extract structured data.
• Translate text.
• Classify intent.
• Help with reasoning tasks.

How LLMs Work at a High Level

LLMs predict the next token based on previous tokens.

The capital of France is ...

Likely next token:

Paris

This simple idea becomes powerful when trained at large scale.

Training vs Inference

Stage	Meaning
Training	Model learns patterns from large datasets
Fine-tuning	Model is adjusted for a narrower task
Inference	Model generates output for a user request

Most application developers use LLMs during inference through APIs or local model runtimes.

LLM Application Flow

flowchart LR
    A["User request"] --> B["Prompt"]
    B --> C["LLM"]
    C --> D["Generated response"]
    D --> E["Application"]

Production systems often add:

• Retrieval.
• Tool calling.
• Safety checks.
• Structured output validation.
• Logging and evaluation.

Common LLM Patterns

Pattern	Purpose
Chat assistant	Interactive conversation
Summarization	Condense long content
Extraction	Convert text into structured fields
Classification	Identify category or intent
RAG	Answer using retrieved documents
Tool calling	Let the application execute actions
Agents	Plan and use tools over multiple steps

Context Window

The context window is the amount of text the model can process in one request.

It includes:

• Instructions.
• User question.
• Chat history.
• Retrieved documents.
• Tool results.
• Generated answer.

Hallucinations

An LLM hallucination is an answer that sounds confident but is not supported by facts.

To reduce hallucinations:

• Use RAG with trusted documents.
• Ask the model to cite sources.
• Validate structured outputs.
• Keep prompts specific.
• Avoid asking the model to guess.

LLM Limitations

LLMs:

• Do not automatically know private enterprise data.
• Can make mistakes.
• Can be sensitive to prompt wording.
• Need guardrails for production use.
• Should not execute business actions without application controls.

Interview Questions

What is an LLM?

An LLM is a large model trained on language data to understand prompts and generate text or structured output.

What is inference?

Inference is the process of sending a prompt to a trained model and receiving generated output.

Why do LLMs hallucinate?

They generate likely text based on patterns, not guaranteed truth. Grounding and validation reduce this risk.

Summary

LLMs are the engine behind many Generative AI applications. They become more reliable when combined with retrieval, tools, validation, security, and observability.