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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAndrej Karpathy’s roughly hour-long presentation, commonly identified as Introduction to Large Language Models, offers a conceptual tour of how LLMs are built, what they may do next, and where their security risks lie. It moves from pretraining and instruction tuning to tool use, the idea of an LLM as an operating-system kernel, and attacks such as prompt injection. This guide explains those ideas in context; the linked video and slides preserve the original presentation and visuals.
What is a large language model?
Karpathy uses Llama 2-70B to make an LLM concrete: think of it as a learned parameters file plus a run file. The parameters file stores learned weights and biases; the run file contains the code that loads them and executes the model. The model generates text by predicting what should come next, based on the text it has already received.
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The “70B” example refers to 70 billion parameters in Llama 2-70B, as described in KDnuggets’ March 4, 2024 summary of the presentation—not a specification that applies to all LLMs. The same summary describes a pretraining corpus of about 10 terabytes of internet text. That, too, is an explanatory figure from the talk’s example, not a universal training-data requirement. KDnuggets’ summary
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The presentation explains assistant training as a sequence: first teach a model to continue text, then train it to follow instructions, and finally refine its responses using preferences. These stages help explain why a model that can write fluent text may still need additional training to behave like a useful assistant.
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Pretraining: learning to continue text
A model is trained on a very large text corpus, using GPU clusters, to predict the next token. This produces a base model capable of generating coherent text. But the training objective does not, by itself, make the model reliably answer a user’s question in the form of a helpful assistant response.
Supervised fine-tuning: learning the assistant format
Next, the model is trained on a high-quality dataset of instructions and answers. This supervised fine-tuning teaches it to respond to requests rather than merely continue text in the style of its training data.
Preference optimization and RLHF: learning which answers people prefer
In the final stage described, candidate answers are compared and training encourages the model to produce responses people prefer. This approach is commonly called reinforcement learning from human feedback, or RLHF. The distinction matters: pretraining builds a text-generation foundation; instruction and preference training shape how that foundation responds to people.
What is the difference between pretraining and fine-tuning?
Pretraining teaches a model broad patterns in text by asking it to predict what comes next. Fine-tuning continues training for a more specific purpose—in this case, responding to instructions in a useful assistant style. Preference training adds another signal by favoring one candidate response over another. They are related stages, but they solve different problems: broad language modeling, instruction following, and response preference.
What does scaling tell us—and what does it not?
The talk presents scaling laws as a tendency for performance to improve as parameter counts and training-data quantities grow, while recognizing practical limits. Bigger models and more data are part of the explanation for capability, not a complete explanation. The training process and the quality and use of data also matter; size alone does not guarantee a useful assistant or address safety concerns.
Can an LLM use tools?
Yes, when a surrounding system gives it access to tools and a way to call them. A browser can retrieve information, a calculator can perform arithmetic, and Python libraries can execute operations that text generation alone may not reliably complete. The model still generates text, but tool access lets the larger system perform actions and use results beyond what the model can infer from its learned patterns.
This changes what must be secured: it is not enough to consider the model’s answer in isolation when the system can also read external content or invoke software.
What does “system one” and “system two” mean for LLMs?
The presentation characterizes current models largely as “system one”: fast, pattern-based responses. It frames slower, more deliberate reasoning—often called “system two”—as a direction for further development. This is a conceptual distinction in the talk, not a guarantee that a model’s fluent explanation reflects a dependable internal process of careful reasoning.
What is an LLM operating system?
Karpathy’s operating-system analogy imagines the model as a kernel process at the center of a system. It can read and write text, access files and software, use tools, generate media, and spend longer periods on deliberate reasoning. In this analogy, the context window is like RAM: the system brings relevant information into the model’s working context and pages other information in or out as needed.
The analogy is a way to think about a capable, tool-using system; it does not mean an LLM is literally an operating system. Its usefulness depends on the surrounding software and permissions that determine what it can access and do.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the security risks?
The talk distinguishes attacks aimed at a model’s safety behavior, the external content it reads, and the data or triggers associated with its training. The distinction is useful because the defenses and failure points differ.
Jailbreaks
A jailbreak attempts to bypass safety controls, for example through role-play, adversarial wording, or optimized text or image sequences. It targets how the model responds to a request.
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Prompt injection
Prompt injection places malicious instructions in content the model may read—such as a web page, image, or document—in an attempt to make it follow an attacker’s directions. Unlike a direct jailbreak, the harmful instruction can arrive as retrieved or supplied content rather than as the user’s explicit request.
For a system with browsing, file access, or other tools, this risk extends to the actions those tools can take. A model may encounter untrusted text while carrying out a legitimate task, so the system must not treat every instruction it reads as authoritative.
Data poisoning, backdoors, and sleeper agents
Malicious examples introduced into training data can create a backdoor: the model behaves differently when a particular trigger phrase or condition appears. “Sleeper agent” describes the concern that such behavior may remain dormant until triggered. This is different from a jailbreak or prompt injection, which targets model behavior during use rather than embedding a trigger through training data.
How to use the presentation
The talk is best treated as a conceptual map of LLM foundations, possible future directions, and security concerns—not as a complete technical course or implementation guide. For Karpathy’s original visuals and demonstrations, use the llmintro.pdf slide deck alongside the video linked from the KDnuggets article.
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