A large language model can be represented by a parameters file and a runtime implementation.

A production large language model is fundamentally the combination of a parameters file containing the model weights and a run-time implementation that executes the model’s forward pass.

  • The parameters are stored as numeric tensors (commonly float16).
  • For very large models this file can be hundreds of gigabytes — for example, a 70 billion‑parameter model stored at two bytes per parameter yields roughly 140 GB.
  • The run-time component can be implemented in C, Python, or other languages and may be only a few hundred lines of code to implement the inference pipeline, tokenization, and sampling logic.

Because inference merely loads weights and performs matrix operations, it can be executed locally (even on consumer hardware for smaller models) without internet connectivity.
However, larger models impose higher latency and memory requirements — for example, a 7B model runs much faster than a 70B model.

Training large language models requires massive text corpora and specialized GPU clusters.

Model training (pre‑training) compresses statistical structure from very large text corpora into model weights and therefore requires both large datasets and significant compute.

  • Typical pre‑training datasets range into multiple terabytes of deduplicated text (the talk cites roughly 10 TB as an order‑of‑magnitude example).
  • Training can require thousands of specialized accelerators run for days to weeks — the talk notes an example run using ~6,000 GPUs for ~12 days at a cost on the order of millions of dollars for a 70B‑class model.

The result is a lossy compression of the training corpus into a compact weight file; the process is analogous to high‑loss, knowledge‑preserving compression rather than lossless archival.
Modern state‑of‑the‑art models often scale dataset and compute factors by an order of magnitude or more, driving costs into the tens or hundreds of millions for leading systems.

The training objective is next-word prediction, which causes the model to encode world knowledge in its weights.

Pre‑training objective: next‑token (or next‑word) prediction — given a token sequence the network predicts a probability distribution over the next token.

  • This objective is computationally tractable and aligns with information‑theoretic compression principles.
  • It causes the model to internalize statistical regularities and factual associations present in the corpus.

Because accurate prediction implies the ability to compress the underlying distribution, the weights implicitly store wide‑ranging knowledge (dates, entities, relations) in an entangled, lossy form.

  • Specific examples from web pages become distributed facts inside parameters rather than verbatim copies.
  • The probabilistic output can be interpreted as confidence over candidate continuations and forms the basis for sampling during inference.

During inference the model samples tokens sequentially, producing text that often resembles training documents but can hallucinate.

Inference proceeds by conditioning on a prompt, sampling a token from the model’s output distribution, appending it to the context, and repeating this left‑to‑right process to generate sequences.

  1. Condition on the initial prompt.
  2. Compute the model’s output distribution for the next token.
  3. Sample a token and append it to the context.
  4. Repeat until the sequence is complete.

Because the model learned from web documents and other sources, generated text often adopts familiar formats (articles, product listings, code) and can produce plausible but fabricated details (e.g., invented ISBNs or synthetic product metadata).

  • Outputs therefore range from accurate, knowledge‑based responses to hallucinations where surface form is correct but factual content is erroneous or invented.
  • It is often unclear whether a specific fact is memorized from training or synthesized from distributed knowledge.

Practical systems treat model outputs probabilistically and rely on downstream verification or tool use to reduce hallucination risk.

Transformer architectures implement the computation but the emergent role of billions of parameters is largely inscrutable.

Transformer networks provide a fully specified mathematical architecture — self‑attention, feedforward layers, layer normalization, and token embeddings — that determines how inputs flow and how intermediate representations are computed.

  • Despite complete knowledge of the architecture and training dynamics, the functional decomposition of many billions of learned parameters is not fully interpretable.
  • Parameters are optimized end‑to‑end to reduce the next‑token loss and the resulting internal mechanisms are emergent and distributed.

Interpretability and mechanistic analysis can reveal localized behaviors in some cases, but many phenomena (one‑way knowledge retrieval, failure modes such as reversal of relational queries) reflect that knowledge representation is anisotropic and task‑dependent.
Consequently, models are best understood as empirical artifacts whose capabilities and failure modes require systematic behavioral evaluation.

Producing an assistant involves fine-tuning a base pre-trained model on high-quality, human-labeled Q&A data.

The production workflow separates pre‑training (broad knowledge acquisition from large, varied corpora) from fine‑tuning (alignment and behavior shaping via curated supervision).

  • Fine‑tuning replaces or supplements pre‑training data with high‑quality, instruction‑style Q&A examples created by human labelers according to detailed labeling instructions.
  • This dataset may be orders of magnitude smaller but much higher in task relevance and quality (an example scale is ~100k dialogue/Q&A exemplars).

Fine‑tuning preserves the underlying factual knowledge while steering output format and style toward helpful, safe assistant behavior.

  • Because it is computationally cheaper than pre‑training, fine‑tuning supports frequent iteration, monitoring, correction of misbehaviors, and incremental deployment.
  • The typical production loop cycles between deployment → behavior monitoring → collecting corrective labels → re‑fining to improve performance.

A further fine-tuning stage uses comparison labels and reinforcement learning from human feedback to align models’ responses.

Beyond direct supervised fine‑tuning, a third stage leverages human preference data that compares candidate model outputs rather than requiring humans to compose ideal outputs from scratch.

  • Labelers rank or choose the better of several model responses; those pairwise comparisons train a reward model.
  • Reinforcement learning or preference‑optimization techniques then optimize the policy to produce outputs with higher expected human preference (commonly called RLHF).

Comparison labeling is often more scalable and easier for humans, and it yields stronger alignment with subjective notions of helpfulness, truthfulness, and harmlessness.
Human reviewers and automated assistants can be combined to accelerate label generation and create hybrid human‑machine workflows for higher throughput and quality.

Model evaluation and ecosystem landscape show proprietary models outperform open-weight models but open models enable customization.

Community evaluation platforms (e.g., human‑judged pairwise comparisons aggregated into an ELO metric) show a performance bifurcation in the ecosystem.

  • Closed‑weight proprietary models typically attain higher performance on many benchmarks but restrict access to weights and deep customization.
  • Open‑weight models (examples include the Llama series and open variants) lag in some benchmarks but expose weights and training details, enabling fine‑tuning, research, and specialized deployment.

The ecosystem therefore bifurcates into high‑performance hosted services and lower‑latency, customizable open stacks; selection depends on requirements for performance, control, cost, and regulatory constraints.

Scaling laws predict smooth improvements in next-word accuracy as model size and dataset scale increase.

Empirical scaling laws show that next‑token loss and downstream metrics are reliably predictable functions of two primary variables: model parameter count N and training dataset size D.

  • These relationships follow power‑law trends and do not show clear saturation within observed ranges, implying that increasing compute and data tends to yield better predictive performance.
  • As a result, provisioning more compute and more data is a validated route to improved capability — which helps explain industry emphasis on larger GPU clusters and bigger curated corpora.

Algorithmic innovations remain valuable as efficiency multipliers, but scaling provides a robust baseline path to capability gains.

Modern LLMs increasingly act as tool-using agents that browse, calculate, plot, and generate images to solve tasks.

Contemporary systems integrate external tools into the text‑generation loop: the model emits structured tool calls (browser, calculator, Python interpreter, plotting libraries, image generators) and then conditions on tool outputs to produce final responses.

  • This offsets limitations of in‑head computation (e.g., accurate arithmetic or up‑to‑date retrieval) and allows orchestration of multi‑step workflows.
  • Tool use converts an LLM from a passive sequence generator into an active agent that leverages existing software infrastructure to increase factuality, reproducibility, and practical utility.

Concrete example workflow:

  1. Browse to collect funding data.
  2. Use a calculator to compute ratios.
  3. Plot results via Python (e.g., matplotlib).
  4. Generate a representative image with an image model.

Multimodality extends LLM capabilities to see, hear, and generate images, audio, and code.

Multimodal models process and produce multiple data modalities — text, images, audio — and can translate between them (for example, generate code from a hand‑drawn UI sketch, caption images, or analyze audio).

  • Multimodal capability enables use cases like image‑to‑code, speech‑based conversational interfaces, and combined audio‑visual content generation and understanding.
  • Integrating modalities increases model flexibility for real‑world tasks and creates richer interaction paradigms beyond text.

However, multimodality also expands the attack surface and complicates evaluation and mitigation strategies.

Researchers aim to add ‘system 2’ capabilities so LLMs can deliberate over longer time horizons and trade time for accuracy.

Cognitive metaphors distinguish fast, heuristic “system 1” behavior from slower, deliberative “system 2” reasoning; current LLMs predominantly implement system 1: quick token prediction without prolonged internal deliberation.

The desired system 2 would allow iterative planning, searching a tree of alternatives, reflecting, and improving answers given more time — converting additional compute/time into higher reliability and accuracy.

Active research directions aim to enable this via architectures and protocols that:

  • Maintain explicit intermediate reasoning states.
  • Support multi‑step verification and chained computations (chain‑of‑thought, tree‑of‑thoughts, reflective loops).

Enabling system 2 behavior requires both modeling changes and interfaces for long‑horizon internal state management.

Self-improvement analogous to AlphaZero’s reinforcement learning remains an open challenge because general reward functions for language are hard to define.

AlphaZero‑style self‑play demonstrates how systems can surpass human performance when there is a compact, automatically computable reward function (e.g., winning a game).

For open‑ended language tasks there is typically no single, fast, unambiguous reward signal that judges output quality across all contexts, which limits straightforward autonomous self‑improvement beyond human imitation.

  • Narrow domains with clear automatic objectives may permit autonomous improvement.
  • General language domains currently rely on human supervision or preference signals; defining scalable, robust reward functions for open‑ended language remains an active and unresolved research problem.

Customization enables domain-specific experts through retrieval, file uploads, and potentially fine-tuning for particular tasks.

Customization levers include instruction tuning (custom system prompts), retrieval‑augmented generation (RAG), and heavier options such as per‑tenant fine‑tuning.

  • RAG lets an LLM condition on user‑provided documents or knowledge bases at inference time, improving factuality in narrow domains without retraining the base model.
  • Platform features (e.g., app stores of specialized assistants) allow assembling purpose‑built agents that combine custom prompts, retrieval, tool integrations, and lightweight model updates to create domain experts.

These methods enable specialization while preserving a common large base model.

LLMs function as a kernel-like orchestration layer analogous to an operating system, with context window as working memory and tools as peripherals.

An operating‑system analogy maps LLM components to classical computing layers:

  • The internet and long‑term storage act like disk.
  • The context window functions as finite working memory (RAM).
  • External APIs/tools serve as peripherals that the LLM orchestrates.

The LLM plays a kernel‑like coordinating role: paging relevant information into the context window, dispatching tool invocations, and orchestrating multi‑step computations to solve user tasks.
Concepts such as multi‑threading, speculative execution, and user/kernel separation have useful analogues in designing LLM orchestration and safety architectures, supporting patterns for memory management, retrieval policies, and modular tool integration.

LLM security faces a cat-and-mouse landscape including jailbreaks that bypass safety via roleplay, encoding, or adversarial suffixes.

Jailbreak attacks exploit the model’s tendency to follow apparent user instructions by reframing malicious prompts as roleplay or encoded content (e.g., asking the model to act as a fictional character that knows disallowed instructions).

  • Encoded inputs (base64, other encodings) can bypass refusal behaviors if refusal training focused on natural‑language examples.
  • Adversarially optimized universal suffixes or perturbations can be transferable jailbreak triggers that induce unsafe outputs across prompts.
  • Carefully crafted adversarial images can encode token sequences that the model interprets as instructions.

Mitigations include broadening safety training to diverse encodings, applying adversarial robustness methods, performing input sanitization, and implementing continual monitoring.

Prompt injection attacks occur when content from external sources embeds instructions that hijack model behavior and can enable phishing or data exfiltration.

Prompt injection arises when an LLM ingests external content (web pages, images, shared documents) that contains instructions designed to override or augment prior directives and thereby change model behavior.

  • Examples: invisible or low‑contrast text in images commanding fraudulent responses; web pages embedding instructions that cause attacker‑controlled outputs; shared documents instructing exfiltration of user data.
  • Tool‑enabled browsing or file retrieval amplifies this risk because the model executes against third‑party content.

Defenses include robust content sanitization, CSP and origin restrictions for tool outputs, limiting tool privileges, verifying provenance, and treating externally sourced content as untrusted.

Data poisoning or backdoor attacks embed trigger phrases or examples in training data to cause deterministic failures under specific inputs.

Data poisoning / backdoor attacks place adversarially crafted examples into pre‑training or fine‑tuning corpora to implant trigger tokens or patterns that activate malicious behavior at inference time.

  • A small insertion during fine‑tuning can create a sleeper trigger (a particular word sequence or token) that causes the model to output erroneous or attacker‑chosen responses when that trigger appears.
  • The attack surface includes publicly scraped web content and outsourced labeling pipelines.

Effective defenses require provenance tracking, dataset auditing, robust training procedures, and post‑training detection and mitigation strategies — this remains an active research and operational security concern.