explainx / blog
Plain-English explanations of the 50 most important AI concepts for business professionals and non-technical people — from LLMs and tokens to RAG, agents, hallucinations, and AI governance. No jargon, just clarity.
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Jun 25, 2026
AI bias is not a glitch — it is a systematic pattern of skewed outputs baked into a model through its training data, design choices, or the way outputs are used. It can cause hiring tools to screen out qualified candidates, lending algorithms to deny loans by zip code, and facial recognition to fail on darker skin tones at higher rates. Understanding the types, causes, and mitigation approaches is now a core skill for anyone building or procuring AI systems.
Jun 27, 2026
Generative AI is the technology behind ChatGPT, Midjourney, and GitHub Copilot — AI that creates new content rather than just analyzing existing data. This foundational guide explains what it is, how it works in plain English, and how to start using it practically, even if you have zero technical background.
Jun 22, 2026
The "who is json" moment is just the surface. Behind every AI product you are building sits 50 concepts your AI tool understands and you probably do not — yet. This is the reference guide: JSON, HTTP, auth, databases, async, state, caching, LLM tokens, embeddings, RAG, context windows, and 36 more. Each concept explained at the depth an AI maker needs, not the depth a computer science textbook requires.
AI discussions have a vocabulary problem. The people building AI systems use precise technical terms that mean specific things. The people making business decisions about AI often nod along without being certain they understand those terms. The gap creates real risk — people approve AI systems they do not understand, avoid AI opportunities because the language feels inaccessible, and ask the wrong questions when evaluating vendors.
This guide covers 50 concepts that come up most often in real AI conversations, explained the way you would explain them to a smart colleague who has not been working in the field. No unnecessary jargon. No oversimplification that strips away the meaning you actually need.
Grouped by theme so you can go directly to the cluster most relevant to your current work.
The broad category: any technique that allows computers to perform tasks that previously required human intelligence — recognizing images, understanding language, making decisions, generating content. AI is the umbrella. Everything else in this list is a more specific thing under that umbrella.
A subset of AI where systems learn patterns from data rather than following explicitly programmed rules. Instead of a programmer writing "if the email contains these words, mark it as spam," a machine learning system is shown thousands of examples of spam and non-spam emails and learns to distinguish them itself. The learning happens from data, not from rules someone wrote by hand.
A subset of machine learning that uses neural networks with many layers (hence "deep"). Deep learning is why AI got dramatically better at image recognition, speech recognition, and language around 2012-2015. Most modern AI systems — including LLMs — are built on deep learning.
An AI system trained on massive amounts of text (books, websites, code, articles) to understand and generate language. GPT-4, Claude, Gemini, and Llama are all LLMs. The "large" refers to both the volume of training data and the size of the model itself (billions or trillions of parameters). LLMs are the technology behind most AI writing, coding, summarization, and Q&A tools.
The mathematical structure underlying most modern AI, loosely inspired by the human brain. A neural network is a system of interconnected nodes (artificial "neurons") arranged in layers. Information flows through the layers, and the connections between nodes are adjusted during training until the network produces accurate outputs. You do not need to understand the math — knowing that "neural network" is the architecture that powers deep learning and LLMs is enough for most business conversations.
The process of teaching an AI model by showing it large amounts of data and adjusting the model's parameters until it gets good at the task. Training is expensive (it can cost millions of dollars in compute for frontier models), time-consuming (weeks to months), and done once (or periodically) rather than continuously. After training, the model's weights are fixed. Do not confuse training with the model learning from your conversation — it is not doing that during use.
The numerical values inside a neural network that determine how it processes inputs. "GPT-4 has 1.8 trillion parameters" means the model has 1.8 trillion numbers that get tuned during training. More parameters generally (but not always) means more capability and more compute to run. When someone says "model weights," they mean this — the trained numerical values that are the model.
A large model trained on broad, general data that can be adapted to many specific tasks. GPT-4, Claude, and Gemini are foundation models — they were not trained to do one thing but to do many things. This is distinct from earlier AI, which was typically trained for a specific narrow task. Foundation models are the basis of the current AI wave.
The unit a language model reads and generates. Tokens are roughly syllables or short words — "unbelievable" is three tokens, "AI" is one, a space plus "the" is one. Models have a maximum number of tokens they can handle at once (the context window). Pricing for API access is typically measured per token — "input tokens" are what you send to the model, "output tokens" are what it generates back. 1,000 tokens is roughly 750 words.
The input you give to an AI model to elicit an output. A prompt can be a question, an instruction, a document to summarize, a conversation history, or all of the above. "Prompt engineering" is the practice of crafting inputs that reliably produce useful outputs. The quality of your prompt significantly affects the quality of the output — AI is not mind-reading, it responds to what you actually write.
The maximum amount of text (measured in tokens) a model can process at once — both what you send it and what it sends back. A model with a 200,000 token context window can process roughly 150,000 words in a single exchange — about a 500-page book. Context window size matters for tasks that require processing long documents, long conversation histories, or large codebases.
Running a trained model to produce an output. Training is the expensive one-time process; inference is the cheaper per-use process of actually using the model. When you send a message to Claude and get a response, you are running inference. "Inference cost" is what it costs to serve each model response — a major operational consideration for AI products at scale.
A setting that controls how random or predictable an AI model's outputs are. High temperature (closer to 1 or above) produces more varied, creative, sometimes unpredictable outputs. Low temperature (closer to 0) produces more consistent, deterministic outputs. For tasks requiring factual accuracy or consistent formatting, low temperature is typically better. For creative writing or brainstorming, higher temperature can be useful.
When an AI model generates a confident, plausible-sounding statement that is factually wrong. Not a lie (the model has no intent) and not a malfunction (it is doing what it was designed to do) — it is a failure mode where statistical pattern-matching produces false information. Citations to papers that do not exist, incorrect statistics, invented biographical details about real people — these are hallucinations. They are reduced by grounding the model in verified documents (see RAG) and human review of consequential outputs.
Connecting an AI model's outputs to verifiable, specific information rather than letting it generate from its training data alone. A grounded model has access to real data — a database, a document, a live search result — and produces outputs that reference that data. Grounding reduces hallucinations because the model is constrained to draw from known-good sources rather than pattern-matching across its training.
A mathematical representation of text (or images, or other data) as a list of numbers that captures its meaning. Embeddings allow computers to measure the similarity between pieces of content — two sentences that mean the same thing will have similar embeddings even if the words are different. Embeddings power semantic search (find me documents similar in meaning to this query, not just documents that share these exact words) and are fundamental to RAG systems.
A technique that improves AI accuracy by having the model search a knowledge base before generating a response. When you ask a question, the system first retrieves relevant documents (using semantic search with embeddings), then passes those documents to the model as context, then generates the answer. RAG lets an AI answer questions from your private data — your documentation, your knowledge base, your CRM — without retraining the model on that data. It also significantly reduces hallucinations by giving the model verified documents to draw from.
Adapting a pre-trained foundation model to a specific task, style, or domain by training it further on a smaller, targeted dataset. Instead of training from scratch (extremely expensive), you take an existing model and continue training it on your specific examples. Fine-tuning is how you get a model that consistently speaks in your brand voice, follows your specific output format, or has deep expertise in your domain. It is cheaper than training but still requires expertise and compute.
An AI system that does not just respond to a single prompt but autonomously plans and executes multi-step tasks, using tools like web search, code execution, file access, or API calls. An agent can be given a goal — "research the top five competitors, pull their pricing pages, and write a comparison" — and work through the steps independently, making decisions along the way. Agents can run for minutes to hours and can spawn sub-agents for parallel tasks.
The coordination layer that manages multiple AI components — models, tools, agents, databases — working together. An orchestration system decides which model to call, in what order, with what context, and how to combine their outputs. LangChain and LlamaIndex are popular orchestration frameworks. For business stakeholders, knowing that "orchestration" is the glue between components helps make sense of architecture diagrams.
The ability for an AI model to call external tools — run a search, query a database, execute code, send an email — as part of generating a response. A model with tool use is not limited to what it knows from training; it can reach out to live systems. When a customer service AI looks up your order status in real time, that is tool use. It is a major capability expansion over models that can only draw on training data.
The technical interface that lets one system call another. When developers say "I'm calling the Claude API" or "we're using OpenAI's API," they mean they are sending requests to the AI provider's servers and getting responses back, programmatically. Understanding that most AI capabilities are delivered via API — not direct software you install — helps frame what "AI integration" actually means in practice.
AI that can process and generate multiple types of content — text, images, audio, video — not just text. GPT-4V, Claude (with vision), and Gemini are multimodal. Multimodal capability means a model can look at a chart and describe it, read an invoice image, or analyze a photograph. For businesses, this opens use cases that text-only AI cannot address.
An open standard for connecting AI models to external data sources and tools in a consistent way. Instead of every developer writing custom code to connect Claude to Google Drive, Slack, or a database, MCP provides a standard interface. Think of it as USB-C for AI tool connections — a universal connector that lets models plug into different systems without custom integration work for each one.
Instructions given to an AI model before the user's input that establish its behavior, persona, constraints, and context. In products built on top of LLMs, the system prompt is how the developer configures the AI — "you are a customer service agent for [company], you only discuss topics related to our product, you always respond in a formal tone." Users typically do not see the system prompt. It is the backstage configuration of every AI product.
A standardized test used to evaluate and compare AI models. Common benchmarks include MMLU (knowledge across 57 subjects), HumanEval (coding ability), and GPQA (graduate-level science questions). Benchmarks are useful for tracking progress and making comparisons, but every benchmark has limitations — models can be trained to perform well on specific benchmarks without generalizing, and benchmark performance does not always predict real-world usefulness for your specific application. When vendors quote benchmark scores, ask what benchmarks are most relevant to your use case.
The percentage of outputs that are correct. Sounds simple, but accuracy has important nuances: overall accuracy can hide disparate performance across subgroups (see Bias), accuracy depends on the specific test set and task, and there is often a tradeoff between accuracy and other metrics like speed and cost. Always ask: accurate on what test set, and for which kinds of inputs?
The time it takes for the AI to produce a response. For conversational AI, users typically expect responses in under two to three seconds. For batch processing of large documents, higher latency is often acceptable. Latency is affected by model size (larger = slower), server load, output length, and infrastructure decisions. It is a key factor in user experience and, for high-volume applications, cost (longer inference = more compute per transaction).
How many requests a system can handle per second or per minute. Distinct from latency (speed per request), throughput is about capacity (total volume). For businesses running AI at scale — processing thousands of documents, serving millions of users — throughput determines whether the system can keep up with demand.
How often a model generates false information on a given task. Not a fixed property of a model (it varies by topic, task, and setup) but a measurable metric for specific use cases. Testing hallucination rate for your specific application before deployment is more informative than general capability benchmarks. Strategies to reduce hallucination rate include RAG, lower temperature, chain-of-thought prompting, and human review.
The verified correct answer against which AI outputs are compared. Evaluating an AI system requires knowing what the correct answer actually is for a set of test cases — that set of correct answers is the ground truth. Building good ground truth datasets for your specific use case is time-consuming but essential for meaningful evaluation. Vendor-provided benchmarks use ground truth datasets that may or may not reflect your use case.
The systematic process of testing an AI system's performance on a defined set of tasks and criteria. Good evaluations test the actual tasks the system will perform in production, cover edge cases and failure modes, disaggregate results by relevant subgroups, and measure the metrics that matter for the business outcome — not just the metrics that are easy to measure. "Setting up evals" is one of the most important (and underinvested) parts of an AI deployment.
Systematic errors in AI outputs that consistently favor or disadvantage certain groups or outcomes. Bias enters AI through training data (which reflects historical inequities), design choices (what to optimize for), and deployment context (using a model outside the population it was trained on). An AI hiring tool trained on historical data will encode historical discrimination. Bias is not random error — it is skewed error that compounds at scale. Reducing it requires intentional data practices, fairness testing, and ongoing monitoring.
The property of an AI system treating different groups equitably. There are multiple mathematical definitions of fairness — demographic parity, equal opportunity, calibration, individual fairness — and they are sometimes mathematically incompatible with each other. "Our AI is fair" is a claim that should be followed by: fair by which definition, for which groups, on which test set. For business procurement, ask vendors which fairness criteria their systems were evaluated against.
The challenge of ensuring AI systems pursue the goals humans actually want, not just the goals as literally specified. An AI optimized to maximize user engagement might achieve that by exploiting psychological vulnerabilities. An AI optimized to minimize customer service resolution time might close tickets without resolving problems. Alignment is about closing the gap between what you specify and what you actually want. At the frontier, it involves harder questions about what happens when AI systems become capable enough to pursue goals independently.
The field concerned with preventing AI systems from causing unintended harm — either through near-term practical failures (bias, hallucinations, security vulnerabilities) or longer-term risks (AI systems that pursue goals humans did not intend and cannot control). For business professionals, AI safety most practically manifests as: what are the failure modes of this system, how severe are they, and what safeguards exist?
Adversarial testing where a dedicated team actively tries to make an AI system fail — produce harmful content, say false things confidently, be manipulated into bypassing its safety guardrails. Red-teaming is how safety problems are found before deployment rather than after. When evaluating an AI system for enterprise use, asking "what red-teaming was done?" and "what failure modes were found?" is a useful indicator of the vendor's rigor.
Techniques users employ to get an AI model to produce outputs it was designed not to produce — by reframing requests, using fictional scenarios, or exploiting gaps in the model's safety training. Not all jailbreaks are malicious (some are safety researchers probing for vulnerabilities), but the potential for jailbreaks is a practical risk for deployed consumer AI products. For enterprise deployments, controls beyond the model itself (input/output filtering, audit logs, usage monitoring) are the practical defense.
Worth noting in the safety context: hallucinations are not just a quality problem, they are a risk management problem. A legal AI that confidently cites a non-existent precedent. A medical AI that confidently recommends an incorrect dosage. A financial AI that cites a non-existent regulation. The confidence with which models deliver false information is what makes hallucinations risky rather than just annoying.
The dataset used to train a model. Training data determines what the model knows, what biases it has, what languages it can speak, and what domains it understands well or poorly. For frontier models (GPT-4, Claude, Gemini), training data consists of trillions of words of text scraped from the internet and licensed sources. For fine-tuned models, training data is the specific examples used to adapt the base model. Understanding what went into the training data is essential for understanding the model's capabilities and limitations.
The requirement that data remain stored and processed within a specific geographic jurisdiction. Relevant for AI because: when you send data to an AI API, it is processed on the provider's servers, which may be in a different country. Healthcare data under HIPAA, EU personal data under GDPR, and government data under various national regulations often have residency requirements that constrain which AI providers you can use or require enterprise agreements with specific data handling guarantees.
Techniques that allow AI systems to learn from or process data without exposing the underlying private information. Differential privacy adds mathematical noise to training data so individual records cannot be reconstructed. Federated learning trains models on data that never leaves individual devices or organizations. Homomorphic encryption allows computation on encrypted data without decrypting it. These techniques matter for businesses that need AI capability but operate on sensitive data.
A documentation standard for AI models that discloses what the model is, what it was trained on, what it was evaluated for, what its known limitations and failure modes are, and what use cases it is appropriate for. Model cards are analogous to nutrition labels — a standard disclosure format that allows downstream users to make informed decisions. When procuring AI systems, asking for a model card (or its equivalent) is a practical due diligence step.
The European Union's comprehensive AI regulation, the first of its kind, which came into force in 2024 with requirements phasing in through 2026. It classifies AI applications by risk level: prohibited (social credit scoring), high-risk (hiring, lending, healthcare, criminal justice, education), limited risk (chatbots that must disclose they are AI), and minimal risk (spam filters). High-risk AI requires bias audits, human oversight, documentation, and registration in a public database. Organizations operating in the EU or selling to EU customers need to understand where their AI applications fall in the risk classification.
The internal policies, processes, and oversight structures an organization uses to ensure its AI systems are used responsibly. Includes: who approves new AI applications before deployment, how AI decisions are audited, what happens when an AI system produces harmful outputs, how AI vendor contracts address liability, and who is accountable when things go wrong. AI governance is becoming a boardroom issue as AI deployments scale and regulatory requirements increase.
The use of AI tools by employees without organizational approval or oversight — the AI equivalent of shadow IT. Employees using personal ChatGPT accounts to process company documents, pasting customer data into consumer AI tools, or using AI writing tools that send content to external servers without data governance controls. Shadow AI is a security and compliance risk because company data is flowing through unapproved systems with unknown data handling policies.
The full cost of an AI system beyond the API or licensing fee: infrastructure to run it, engineering time to integrate and maintain it, evaluation and monitoring costs, retraining costs over time, and the human oversight costs for reviewing AI outputs in high-stakes applications. AI projects routinely underestimate TCO because the initial API fee is small but the operational overhead is substantial. Modeling TCO before deployment is essential for accurate ROI assessment.
The return on investment from AI deployment. Calculating AI ROI requires: quantifying the value delivered (time saved, error rate reduced, revenue generated, cost avoided) and dividing by total cost (development, deployment, maintenance, oversight). Common pitfalls: using vendor-provided benchmark improvements rather than measuring business outcomes, not accounting for TCO, and not measuring baseline performance before deployment so there is nothing to compare against. Rigorous AI ROI requires measuring before, during, and after deployment.
AI that creates new content — text, images, audio, video, code — rather than just classifying or predicting. GPT-4 generating an email draft, Midjourney generating an image, Sora generating a video, GitHub Copilot generating code — these are all generative AI. Generative AI is the category that has driven the current wave of enterprise AI interest because it enables knowledge work automation in a way that classification and prediction models did not.
AI systems that operate with greater autonomy over longer time horizons, making decisions and taking actions independently rather than responding to single prompts. An agentic system can be given a goal, plan how to achieve it, use tools to execute the plan, check its own work, and adjust based on results — without a human guiding each step. This is the direction current AI is moving: from "answer this question" to "complete this project." Claude Tag (which can wait days for a blocking condition before executing a task) is an example of agentic AI in a team setting.
Knowing these 50 terms is most useful as a foundation for three things:
Asking better questions. When a vendor says their model has "high accuracy," you can now ask: "Accuracy on what benchmark? Disaggregated by which subgroups?" When they say "built with RAG," you can ask: "What knowledge base, how is it kept current, and what is the hallucination rate for our specific document types?"
Evaluating proposals more critically. AI proposals often conflate what a model can do in demos with what it will do reliably in production. The concepts in Section 4 (benchmarks, accuracy, latency, evals) give you the vocabulary to push for real performance data rather than demo performance.
Governing AI use in your organization. The concepts in Section 7 (governance, shadow AI, TCO, the EU AI Act) give you the framework for the organizational side — not just "does this AI work" but "is our organization using AI responsibly, legally, and with appropriate controls."
None of these 50 concepts requires a technical background to understand. They require someone explaining them plainly — which is what this guide is for.
For deeper dives on specific concepts, see our guides on AI bias, Claude Tag use cases, and the technical AI concepts guide for business leaders.
