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Mejix · Executive Briefing
A non-hype briefing on how LLMs are built, where they fail, and where to focus your AI investment.
Built for leadership. 10 slides. Ten minutes.
02 / 10 · Mental Model
Strip away the marketing and an LLM reduces to two artifacts on disk. Understanding what they are is what lets you reason about hosting, licensing, and risk.
File 1 · Weights
The knowledge — hundreds of gigabytes.
A compressed snapshot of the internet, learned during pre-training. This is what costs tens of millions of dollars to create.
File 2 · Runtime
The logic engine — a few hundred lines of code.
Surprisingly small. It loads the weights and predicts one token at a time. That's it.
When you "license a model," you are licensing the weights file. Everything else — guardrails, retrieval, tools, workflows — is what your team builds around it.
Source: ai.gopubby.com
03 / 10 · Phase 1
The expensive part. Done once, by a few companies, and shipped to the rest of the world as a "base model."
01
Hundreds of billions of web pages — over a petabyte of raw data — squeezed into a few hundred GB of weights.
02
Tens of thousands of GPUs for months. Only a handful of players: Google, OpenAI, Meta, Anthropic, xAI.
03
Toxic content removed, duplicates dropped, high-quality sources up-weighted. Garbage in = garbage out at scale.
04
It's a document generator. Ask it a question and it might just give you back more questions.
04 / 10 · Phase 2
Pre-training gives the model knowledge. Alignment gives it behavior — and this is the stage where your organization gets leverage.
Supervised Fine-Tuning (SFT)
Curated, human-written examples of question → ideal answer. The model learns the format, tone, and structure of a helpful response.
The strategic line
Pre-training installs general knowledge. Alignment installs behavioral skills — how to follow instructions, when to refuse, what tone to use, how to escalate.
You can buy the first. You have to build the second.
05 / 10 · RLHF
Reinforcement Learning from Human Feedback is the layer that turns a raw assistant into something you can put a brand name on.
01
Given the same prompt, raters choose which of two model responses is better.
02
Their choices train a smaller model to predict what humans would prefer.
03
The main model is tuned to maximize the reward — fewer hallucinations, less toxicity, more helpfulness.
For the C-suite
RLHF is your brand-safety control surface.
Whoever sets the rater guidelines decides what "helpful" means in your assistant.
06 / 10 · Vision
The most useful framing of where this is going: the LLM is the operating system. Tools are syscalls. Your enterprise data is the file system.
A model that can call tools beats a bigger model that has to guess every time. This is why "agentic" workflows matter more than raw parameter counts.
Don't guess at arithmetic — call it.
Don't recall facts — go look them up.
Don't simulate code — run it.
Don't memorize your data — query it.
07 / 10 · Cognition
Why today's models hallucinate, and what the next generation does about it.
System 1
The model is forced to answer immediately, one token at a time. There's no time to "think" — so when it doesn't know, it fills in the most plausible-sounding next word. That's a hallucination.
System 2
Reasoning models think before they speak — generating, checking, and revising an internal scratchpad before showing an answer. Slower and more expensive per query, but materially more accurate on hard tasks.
08 / 10 · Enterprise scale
Three constraints that shape every enterprise AI architecture conversation.
Training data
1+ PB
Raw web pages crawled
Filtered down to hundreds of billions of high-quality tokens before training begins.
Training cost
$10s of M
Per frontier model
Tens of thousands of GPUs running for months. Done from scratch only by a few players.
Context window
128K – 1M
Tokens the model can read at once
The real enterprise constraint. Bigger windows enable richer RAG, longer documents, deeper memory.
09 / 10 · Security
Three classes of threat your security team needs a policy for — today, not next quarter.
Crafted prompts that bypass the model's safety alignment and make it produce content it was tuned to refuse.
Malicious instructions hidden inside documents, webpages, or emails the model reads — turning your assistant against you.
Adversarial data slipped into the training or fine-tuning set, creating backdoors that only trigger on specific inputs.
AI security is a data problem as much as a software problem.
Treat untrusted text the way you treat untrusted code. Isolate it, sanitize it, and never let it reach a tool with real-world side effects without a human in the loop.
10 / 10 · The path forward
"LLMs are inscrutable artifacts. We don't fully understand why they work, only that they do — and that we can shape them at the edges."
01
Invest in data governance — it's the input quality bound.
02
Invest in tool alignment — calling tools beats guessing every time.
03
Don't assume the model is a magic brain. Assume it's a brilliant intern with no memory.
Or browse the rest of the AI Solutions series.