What Is LLM in AI?

LLMs Are Everywhere — But Few Can Explain Them

Open ChatGPT to draft an email, ask Claude to review a contract, and let Gemini summarize a meeting — for millions of professionals, these tools are now as routine as email itself. Yet ask the same professionals what a "large language model" actually is, and most go quiet.

A large language model is a type of AI model — typically a neural network — trained on vast amounts of text to perform natural language processing tasks, especially generating language. Modern LLMs are built on the transformer architecture, introduced in Google's 2017 paper "Attention Is All You Need," whose attention mechanism processes relationships between words regardless of their distance in a sentence. This is why all three flagship assistants — ChatGPT, Gemini, and Claude — are described by UC Berkeley's I School as transformer-based large language models.

Under the hood, the process is simpler than most people expect. LLMs are pre-trained to predict the next word (or token), then fine-tuned — often with human feedback — to follow instructions. A few concepts help demystify the jargon:

• Tokens: the chunks of text a model reads and writes. Roughly 1,000 tokens equals 750 words.
• Context window: the maximum text a model can process in one interaction — flagship models now offer windows of one million tokens.
• "Large": an intentionally vague qualifier. There is no fixed parameter threshold that makes a model "large."

Why does understanding this matter now? Because the money has arrived. According to Menlo Ventures' mid-2025 market research, enterprise LLM spend more than doubled from $3.5 billion in November 2024 to $8.4 billion by mid-2025 — a seven-month surge that signals these systems have moved from experiment to infrastructure.

That shift has consequences. As LLMs evolve from chatbots into agents that use external tools and execute multi-step workflows, the skills and instructions attached to them become part of your security and quality surface. That's the problem SkillzBench is being built to solve: a free directory and MCP connector that verifies, scans, and benchmarks agent skills before you trust them in production.

If you're adopting AI at work, you don't need to build a model — but you do need to understand what one is, what it can't do, and what you're attaching to it. The rest of this article covers exactly that.

How LLMs Work — and Why 'Which Model?' Is the Wrong Question

Every LLM starts the same way: a transformer neural network trained to predict the next word across enormous volumes of text. That pre-training gives the model raw language ability; a second stage — fine-tuning via techniques like reinforcement learning from human feedback (RLHF) or Anthropic's Constitutional AI — teaches it to follow instructions, refuse harmful requests, and respond usefully. All three flagship assistants you likely know, ChatGPT, Gemini, and Claude, are transformer-based LLMs built this way, according to a UC Berkeley comparison.

But knowing how models are built doesn't answer the question most readers actually have: which one should I use? Berkeley's guidance cuts through it — "the honest answer to 'which LLM should I use' is not which model is best, but which model is best for what." The differences are real and measurable.

Consider how the major families actually diverge:

• Pricing varies 2.5x on input tokens. As of Berkeley's July 2026 comparison, Gemini 3.0 Pro charges $2.00 per million input tokens, Claude Sonnet 4.6 runs $3.00/M, and GPT-5.5 costs $5.00/M input plus $30.00/M output.
• Context windows have converged. All three flagships now offer 1M-token windows — roughly 750,000 words processed in a single interaction.
• Code generation is Claude's stronghold. Anthropic holds 42% of a $1.9B code-gen market versus OpenAI's 21%, per Menlo Ventures' mid-year market update.
• Reasoning models think before answering. OpenAI's o1 (2024) generates long chains of thought before responding, trading speed for accuracy on hard problems.
• Open-weight alternatives exist. DeepSeek R1, a 671-billion-parameter open model released in January 2025, performs comparably to o1 at a much lower price per token.

Despite the open-weight buzz, the enterprise reality is decisively closed: 87% of enterprise workloads run on closed models, with open-source share falling from 19% to 13% in six months. Enterprise market share breaks down as Anthropic 32%, OpenAI 25%, Google 20%, Meta's Llama 9%, and DeepSeek 1%. Notably, only 11% of teams switched vendors last year — 66% simply upgraded within their existing provider.

For builders, the practical takeaway is that model choice is a per-task decision, not a loyalty program. And as LLMs increasingly act as agents — planning steps and calling external tools — the quality of what you connect them to matters as much as the model itself. That's the gap SkillzBench is addressing: a free, verified directory of agent skills plus a free MCP connector, with security scanning and sandbox benchmarking so you can test skills before you trust them.

The Agentic Shift: From Chatbot to Digital Worker

The most important change in how LLMs work isn't a bigger model — it's a different job description. When an LLM is enabled to think step-by-step, reason through problems, and use external tools across multiple interactions, it becomes what researchers call an agent, and Menlo Ventures' market research finds this makes LLMs dramatically more effective for real-world applications.

McKinsey frames the same shift as foundation models "capable of acting in the real world, planning and executing multiple steps in a workflow." In practice, that means an LLM that once answered a single question can now research a topic, call an API, check its own work, and loop until the task is done. Anthropic's Model Context Protocol (MCP) is the open standard making this practical — a common way to connect models to external tools and data, regardless of which assistant or coding agent you use.

For business owners, this is the difference between a chatbot and a digital worker. For agent builders, it's why the ecosystem around models now matters as much as the models themselves. The supporting trends reinforce it:

• Multimodal LLMs that process images, audio, and more are growing at a 28.95% CAGR, expanding what agents can perceive and act on.
• Domain-specific models such as NASA and IBM's INDUS suite for Earth science show agents tuned to specialized fields, with domain-specific APIs displacing generic ones in banking and healthcare.
• Code generation — the natural home of agentic workflows — has become a $1.9B ecosystem, and enterprise LLM spend more than doubled from $3.5B in late 2024 to $8.4B by mid-2025.

But granting an LLM tools and multi-step autonomy also raises the stakes. LLM security assumes the model might be tricked into treating untrusted input as instructions, and as agents take on more of a workflow, the quality and safety of every tool and instruction package they load becomes part of your risk surface. Verification can't be an afterthought when the agent only sees what you connect it to.

That's exactly the gap SkillzBench is building for. Agent skills — SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents — extend what any LLM can do, but not all of them help: published research found roughly 26% of skills carry at least one vulnerability. SkillzBench is creating a free, independent directory of agent skills plus a free MCP connector, with every skill pinned to a commit SHA, security-scanned, and benchmarked in a sandbox to show its Lift in percentage points — so you can see what a skill actually adds before your agent relies on it.

The agentic shift is here; the discipline to match it is what comes next. Skills, proven. Join the SkillzBench waitlist to be first to browse, verify, and test agent skills free — before you trust them with your workflows.

Trust but Verify: Hallucinations, Prompt Injection and the New Security Model

For all their fluency, large language models fail in ways traditional software never did. They can confidently state falsehoods, absorb bias from training data, and — most dangerously — be manipulated into treating untrusted input as commands. Understanding these failure modes is now part of understanding what an LLM actually is.

The first limitation is the hallucination problem. Because an LLM predicts plausible text rather than retrieving verified facts, it can generate inaccurate output with complete confidence. Biased or inaccurate training data compounds this, and Grand View Research lists hallucinations, bias, and data privacy among the key market restraints slowing enterprise adoption. The practical rule from UC Berkeley's I School is blunt: any LLM output going into a production codebase warrants human review.

The security picture is even more fundamental. As cloud security firm Wiz explains, traditional app security assumes inputs are untrusted; LLM security assumes inputs are untrusted and that the model might be tricked into treating untrusted input as instructions. In other words, data can become code. The OWASP Top 10 for LLM Applications — developed by more than 600 experts — ranks prompt injection, training data poisoning, and supply chain vulnerabilities among the top risks, and MITRE ATLAS now documents over 130 documented attack techniques against AI systems.

Organizations are struggling to keep up:

• 31% of organizations cite a lack of AI security expertise as their top challenge, per Wiz's research.
• Prompt injection remains the most cited risk in the OWASP Top 10 for LLM Applications.
• Supply chain vulnerabilities — risky third-party components plugged into models — rank alongside injection in the OWASP list.
• MITRE ATLAS catalogues 130+ attack techniques but only 26 mitigations, a stark gap between attack and defense.

This matters most as LLMs become agents. When a model gains tools and skills — instruction packages that tell it how to use external capabilities — every one of those components becomes a potential attack surface. The numbers back this up: published research found that roughly 26% of agent skills carry at least one vulnerability. Nobody would run unverified plugins in production software; the same standard should apply to the skills your agent loads.

That verification gap is exactly what SkillzBench is being built to close. Its planned free directory will continuously crawl and dedupe skills from GitHub, pin every version to a commit SHA, run security scans and license checks, and publish a trust score from 0–100 — with benchmark Lift shown separately so you can see what a skill actually contributes before you rely on it. A free MCP tool will connect any agent to only the skills you explicitly choose.

LLMs are only as good as the skills you give them. Skills, proven. Join the SkillzBench waitlist to verify and benchmark agent skills before you trust them — the directory, MCP connector, and benchmarks are all free.

Getting Started: Using LLMs Wisely in Your Workflow

Knowing what an LLM is only matters if you can put one to work without getting burned. The good news: a few practical habits separate teams that get real value from teams that get burned.

Start by abandoning the search for the single "best" model. As UC Berkeley's I School puts it, the honest answer to "which LLM should I use" is not which model is best, but which model is best for what. A fast, cheap model suits summarization; a reasoning model suits complex analysis; a coding-focused model suits your codebase. Menlo Ventures found that builders move en masse to the best-performing model rather than clinging to older, cheaper ones — so re-evaluate per task, not per vendor.

Second, keep a human in the loop for anything production-bound. LLMs hallucinate, inherit bias from training data, and can be tricked by prompt injection. Berkeley's guidance is blunt: any LLM output going into a production codebase warrants human review. Treat generated code, customer-facing copy, and legal or financial text as drafts, not deliverables.

Third, treat connected tools as part of your attack surface. LLM security differs fundamentally from traditional security: the model itself can be tricked into treating untrusted input as instructions, and 31% of organizations cite lack of AI security expertise as their top challenge. The more tools, data sources, and agent skills you bolt on, the wider that surface gets.

A sensible starting checklist:

• Pick models per task — match capabilities to the job, not the hype cycle
• Route all production-bound output through human review
• Pin and verify every third-party skill or tool your agent can touch
• Track upstream changes so a "safe" tool doesn't silently become a risky one

That last two points are where SkillzBench comes in. We're building a free, independent directory of AI agent skills plus a free MCP connector that links any agent to the skills you choose — with every version pinned to a commit SHA, security-scanned, license-checked, and benchmarked in a sandbox so you can see the Lift each skill actually delivers. Since the platform is still in progress, the best way in is the waitlist: you'll be first to browse, test, and equip verified skills as they go live.

LLMs are only as good as the skills you give them. Skills, proven.