Most modern AI products are built from

the same set of core ideas. In the next

seven minutes, I'll walk through nine

concepts you will see repeatedly across

real world AI systems. One,

tokenization. Neural networks like LMS

cannot work with raw text directly. A

tokenizer breaks text into smaller units

called tokens and maps each token to an

integer ID. So, the model can take the

sequence as input instead of raw text.

The most common algorithm is bite pair

encoding or BPE. BPE starts from small

units often bytes or characters and

repeatedly merges the most frequent

adjacent pairs to form new tokens. Over

time, common fragments like ing or ti

become single tokens. So words like

walking might be split as walk plus ing.

Two, text decoding. An LLM simply

outputs a probability distribution over

the vocabulary for the next token. A

decoding algorithm chooses one token

from that distribution, appends it to

the sequence, and repeats the process to

produce a full response. The simplest

text decoding approach is greedy

decoding, which always picks the most

likely next token. It can work well for

deterministic tasks, but not for tasks

requiring creativity. Sampling based

methods add controlled randomness to

improve diversity. For example, top P

sampling draws the next token from the

smallest set of tokens whose

probabilities sum to P, then samples

from that set. Three, prompt

engineering. Vake prompts usually lead

to vague answers. Prompt engineering is

the practice of shaping instructions and

context to steer a model's behavior

without changing its weights. A strong

prompt clearly states the task key

constraints and expected output format.

One common technique is fshot prompting

where you include a handful of examples

so the model imitates the desired style

and structure. Another is chain of

thought prompting which you ask for

step-by-step reasoning. Coot prompting

can improve performance on problems that

require multi-step logic like math and

coding. Prompt engineering is widely

used because it is fast to iterate on

and inexpensive compared to training or

fine-tuning a model. Four, multi-step AI

agents. An LLM on its own only generates

text. It cannot take actions like

browsing the web, checking the weather,

or running code. Multi-step agents wrap

an LLM in a loop with access to tools

and memory. So it can plan what to do

next, call external tools, and use the

results to decide the next step. The

agent repeats this cycle until it

reaches the goal, runs out of a budget,

or determines it cannot make further

progress.

Five, retrieval augmented generation. A

plain LLM answers using only what is

stored in its weights. So it can be

wrong or outdated on recent events or

changing company policies. Rag pairs an

LLM with a retrieval system connected to

a knowledge store. When you ask a

question, the retriever first pulls

relevant passages from sources like

PDFs, docs, or a database. Then the LLM

uses those passages to write the answer.

This grounds the response in external

evidence instead of relying only on the

model's memory.

Six, reinforcement learning from human

feedback. The initial launch of Chad GPT

succeeded in large because of the RLHF

stage. RLHF is a reinforcement learning

approach where the model practices by

generating multiple candidate responses.

A separate reward model scores them and

the training algorithm updates the

model's weights. So higher scoring

responses become more likely over time.

This pushes the model toward outputs

that people consistently rate as more

helpful, clear, and safe, not just

outputs that are statistically likely.

RLHFs align an LLM with human

preferences, mainly because of how the

reward model is trained. The reward

model learns directly from human

feedback, usually from pairs of model

responses to the same prompt where

annotators pick the one they prefer. By

learning these preference patterns, the

reward model becomes a proxy for what

humans tend to want and reinforcement

learning uses that signal to steer the

LLM toward responses that score higher

on that proxy.

Seven, variational autoenccoder. A VAE

is a generative modeling approach that

learns a probability distribution of

data. A VAE consists of two neural

networks, an encoder and a decoder. The

encoder maps the input into a

lowdimensional latent representation

while the decoder maps the latent vector

back to the original input space.

Training optimizes a reconstruction

objective so the decoded output stays

close to the original input. After

training, new data can be generated by

sampling a point from the latent space

and decoding it. In modern text to image

and texttovideo systems like OpenAI's

Sora, a VA is often used as a latent

compressor, allowing the downstream

model to operate more efficiently in a

smaller space.

Eight, diffusion models. Diffusion

models generate data by learning to

reverse a gradual noising process.

During training, you take real samples

like images, add noise over many time

steps, and train a model to predict the

noise given the noisy input. the time

step and optional conditioning such as

text. At inference time, you start from

pure noise and repeatedly apply the

learn the noising step to move toward a

clean sample.

Nine, low rank adaptation. Large models

like LLMs and textto image systems are

general purpose. They handle broad

everyday tasks well, but often struggle

in specialized domains. Laura is an

efficient fine-tuning method that adapts

a pre-trained model without updating all

of its parameters. It keeps the original

linear layer weights frozen and adds two

small low rank trainable matrices. So

the model can learn a domain specific

adjustments with far fewer new

parameters. With this foundation, you

should find reading future AI designs

and articles much easier.