Large Language Models (LLMs)

What it is

A large language model is a neural network trained on very large amounts of text (and, increasingly, images, audio and code) to predict the next token. That simple objective, at scale, produces models that can summarize, translate, write code, answer questions, extract structured data, reason through multi-step problems and decide when to call external tools.

For an enterprise, the LLM is the engine, not the product. The value comes from what you connect it to — your data, your workflows, your guardrails — and from choosing the right model for each job on quality, latency, cost and data-residency grounds.

How it works

1. Tokenize

   Input text is split into tokens (word pieces). Pricing, speed and context limits are all measured in tokens.

2. Attend

   Transformer layers use self-attention to relate every token to every other token in the context window.

3. Predict

   The model outputs a probability for every possible next token; sampling settings such as temperature decide which one is chosen.

4. Repeat

   Each generated token is fed back in until the answer is complete, a stop sequence is hit, or the model emits a tool call.

5. Align

   Instruction tuning and preference training (RLHF and similar) turn a raw text predictor into a helpful, safer assistant.

Key concepts

Context window

How many tokens the model can consider at once — prompt, documents, conversation history and its own output combined.

Sampling

Temperature and top-p trade creativity for consistency. Extraction and classification want low temperature; brainstorming wants higher.

Reasoning models

Models that spend extra tokens thinking before they answer, improving accuracy on math, code and planning at the cost of latency.

Tool calling

The model returns a structured request to run a function (search, database query, API call) instead of plain text.

Structured output

Constraining responses to a JSON schema so downstream code can parse them reliably.

Hosted vs. open-weight

Frontier APIs offer the highest capability; open-weight models can be self-hosted for cost, privacy or customization.

Hallucination

Fluent but unsupported output. Mitigated with grounding (RAG), citations, validation and evaluation — not eliminated.

Latency and cost

Driven by model size, input and output tokens, and caching. Routing easy requests to smaller models is a common saving.

Tools & platforms

Skills employers look for

• Explaining transformers, tokens, context windows and sampling to technical and business audiences.
• Selecting models against quality, latency, cost, context length and data-residency requirements.
• Integrating model APIs with streaming, retries, rate limits, timeouts and structured outputs.
• Controlling cost with prompt caching, batching, model routing and token budgets.
• Deploying open-weight models on GPUs with an inference server, and knowing when that beats an API.
• Recognizing hallucination risk and designing validation around model output.

Enterprise use cases

Common pitfalls

• Choosing a model by leaderboard alone — benchmarks rarely match your data; test on your own examples.
• Ignoring token economics — long prompts, verbose outputs and no caching can multiply cost.
• Trusting output without checks — validate structure, cite sources and keep humans in the loop for high-stakes decisions.
• Hard-wiring one vendor — a thin abstraction layer keeps you free to switch models as they improve.