AI: How It Works (In Plain English for Operators)

Most “AI” confusion comes from mixing three very different things:

A model (the math that predicts outputs)
A product (a UI, workflow, integrations)
An outcome (support tickets resolved, leads generated, code shipped)

Operators care about outcomes. So here’s AI: how it works in plain English, with the minimum theory you need to make good decisions in 2026.

The simplest mental model: AI is a prediction machine

At its core, modern AI is a system that predicts what should come next.

In image AI, it predicts which pixels belong together.
In fraud detection, it predicts whether a transaction is suspicious.
In a chatbot, it predicts the next word (more accurately, the next token).

This matters because it explains both the power and the failure modes:

AI is great at patterns it has seen many times.
AI is weak at guarantees, especially when you need certainty, citations, or real-world verification.

How large language models (LLMs) work (without the math)

An LLM is trained on a huge amount of text and learns statistical relationships between pieces of language.

When you type a prompt, the model:

Breaks your text into tokens (chunks of characters/words).
Computes which tokens are most relevant to each other using a mechanism called attention.
Produces a probability distribution over possible next tokens.
Samples a next token (more deterministic when temperature is low).
Repeats until it reaches a stop condition.

If you want the deeper technical background, the foundational architecture is the Transformer from the paper “Attention Is All You Need”.

A plain-English glossary for operators

Term	What it means in practice	Why you should care
Token	Chunk of text the model reads/writes	Costs and limits are priced in tokens
Context window	How much text the model can consider at once	“Memory” is often just context size
Temperature	Randomness in outputs	Higher = more creative, lower = more consistent
System prompt	The highest-priority instruction	Defines tone, constraints, and boundaries
Hallucination	Confident-sounding wrong output	Requires verification and guardrails
Tool calling	Model triggers an external action (search, DB query)	Turns chat into workflows

Training vs inference: where the behavior actually comes from

Operators often treat AI like a person learning on the job. Most production systems do not work that way.

Training (learning the patterns)

Training is when the model’s parameters are learned from data. This is expensive and done by model providers (or large teams) because it requires significant compute, data pipelines, and evaluation.

Inference (using the model)

Inference is when you send a prompt and get an output. This is what you pay for day-to-day, and what drives latency and cost.

Three ways to shape outputs (and when to use each)

Method	Best for	Tradeoffs
Prompting	Fast iteration, clear constraints, structured outputs	Can be brittle if inputs vary
Fine-tuning	Stable style and narrow tasks at scale	Requires training data and ongoing evals
Retrieval (RAG)	Answers grounded in your docs, fresh facts	Needs good indexing and relevance tuning

In most operator use cases, the winning sequence is prompting first, then retrieval, and only then consider fine-tuning if you have stable tasks and enough data.

“Memory” is usually not memory: it’s context plus retrieval

When people say “the AI forgot,” what’s usually happening is one of these:

The required info scrolled out of the context window.
The prompt did not re-state key constraints.
The model never had the information (and guessed).

If you need consistent, factual answers, you typically add retrieval.

Retrieval in one paragraph

Retrieval systems store your knowledge (docs, past tickets, product pages, internal notes) in an index. When a query comes in, the system pulls the most relevant snippets and injects them into the prompt so the model can answer using that context.

This is why many serious “AI products” are not just a model. They are a model plus a knowledge pipeline plus a workflow.

If you want a formal risk framing for deployed AI systems, the NIST AI Risk Management Framework is a solid operator-oriented reference.

Why LLMs sometimes lie (and how to think about it operationally)

LLMs do not have a built-in truth meter. They optimize for “a plausible continuation,” not “a verified answer.”

Common failure modes you should plan for:

Hallucinated specifics (dates, numbers, quotes, features)
Context drift (answers the wrong question because the prompt was ambiguous)
Over-generalization (sounds smart, misses constraints)
Inconsistent outputs (same input, different answers) when temperature is high or instructions are weak

The operator move is not “tell the model not to hallucinate.” The move is to design a system where hallucinations are either:

Impossible (answer only from retrieved sources and quote them), or
Low-stakes (drafting, summarization, ideation), or
Caught (review gates, automated checks, or both)

Redditor AI’s blog has a deeper, practical review framework for this in Questioning AI: Tests for Trustworthy Replies, if you want a field checklist.

What “AI agents” actually are (in production terms)

An “agent” is usually just an LLM placed inside a loop that can:

Observe inputs (messages, alerts, queue items)
Decide what to do next
Call tools (search, write draft, update a CRM)
Repeat until a stopping rule is met

The important part is not the word agent. It’s the loop, the tools, and the stopping rules.

A useful operator definition

Agentic AI = LLM + tools + policy + memory (context/retrieval) + evaluation.

Without policy and evaluation, agents become unpredictable automation.

The operator’s checklist: what to define before you “add AI”

Most AI projects fail for the same reason automation projects fail. The work is not well specified.

Before you ship anything, define these six items:

1) The unit of work

Examples:

“Classify inbound messages into 8 categories.”
“Draft a first response using our knowledge base.”
“Extract intent signals from a thread.”

If you cannot name the unit of work, you cannot measure it.

2) Inputs and boundaries

What fields are available every time?
What is explicitly out of scope?
What should the model do when it lacks information?

3) A success metric tied to the business

Good:

Time to first useful draft
Resolution rate with human review
Cost per qualified lead

Risky (because it invites vanity optimization):

Number of messages sent
Number of tokens generated

4) A baseline

If humans currently do the task, time it and sample quality. AI ROI is easiest when you can compare before vs after.

5) An evaluation plan

You need at least one of:

Human scoring on a rubric
Automated checks (format, banned claims, required fields)
Outcome metrics (conversion, resolution)

6) A cost model

Inference is not free, and in 2026 it still surprises teams. Track:

Average tokens per task
Model choice per task (routing expensive models only where needed)
Cost per outcome (not per message)

For a deeper operator take on costs and moats, see The Business of AI: Costs, Moats, and GTM That Matter.

A concrete example: AI applied to demand capture on Reddit

To keep this article focused, here’s the high-level mapping (not the full playbook).

In a Reddit customer acquisition workflow, AI typically helps with:

Monitoring: continuously finding relevant conversations across subreddits.
Relevance filtering: separating “interesting” from “actionable.”
Drafting: producing a first-pass reply that matches the thread context and your brand.
Promotion (when appropriate): including your brand as an option without turning every reply into an ad.

That’s exactly the category Redditor AI sits in: AI-driven Reddit monitoring plus automatic brand promotion, with URL-based setup to find relevant conversations and automate customer acquisition.

If you want the step-by-step revenue workflow, those are covered elsewhere on the site, for example:

Buy vs build: the only question that matters

Building is tempting because LLM APIs are easy to call. Operating a reliable system is the hard part.

A good buy vs build filter is:

Build if the workflow is a core differentiator and you have sustained engineering and evaluation capacity.
Buy if the workflow is repeatable, the value is in operational consistency, and you want time-to-outcome.

For Reddit demand capture specifically, the differentiation is usually not “we can call an LLM.” It’s coverage, relevance, routing, consistent engagement, and measurement across a noisy channel.

The bottom line

AI works by predicting outputs from patterns. LLMs work by predicting the next token given context. Real business value appears when you wrap models in retrieval, tools, policies, and evaluation, then aim them at a measurable unit of work.

If you’re an operator, your edge is not knowing more jargon. Your edge is designing workflows where AI’s strengths (speed, pattern matching, drafting) compound, and its weaknesses (truth, consistency, overconfidence) are contained.

If that workflow for you is “turn Reddit conversations into customers,” you can start with Redditor AI here: redditor.ai.