Jargon Buster

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AI & LLMs

Working with language models and AI systems.

The territory

30 core terms mapped for this field, ranked by how often builders reach for them. Each one is a future entry. Want to bust one? One entry, one file, one pull request.

  • PromptInput or instruction sent to a model for a response"what I type in" · "the question I ask it"
  • System promptPersistent instruction block that sets a model's role and rules"the hidden instructions" · "the personality setup text"
  • Context windowMax tokens a model can hold in working memory at once"how much it can remember" · "the size limit of the conversation"
  • RAG (retrieval-augmented generation)Fetch relevant documents, inject them into the prompt before answering"make it read my docs" · "let it search my files first"
  • EmbeddingNumeric vector representing meaning, enabling similarity search"turning text into numbers" · "the meaning fingerprint"
  • Vector databaseStore searching by semantic similarity rather than keywords"search by meaning not words" · "the AI memory store"
  • ChunkingSplitting documents into retrievable pieces before embedding"cutting docs into bits" · "how big the snippets are"
  • Tool calling / function callingModel emits structured requests your code executes and returns"let it press buttons" · "make it use my API"
  • Structured outputForcing responses into a strict JSON or schema shape"make it always return JSON" · "stop it rambling, give me fields"
  • AgentLLM looping over tools and observations toward a goal"AI that does stuff on its own" · "it keeps going till done"
  • MemoryPersisted facts recalled across separate sessions"it remembers me" · "saves what I told it last time"
  • HallucinationConfident, fluent output that is factually invented"it made that up" · "it lies convincingly"
  • TemperatureSampling knob trading determinism for creativity and variety"how random it gets" · "the creativity dial"
  • TokenSub-word unit models read, generate, and bill by"chunks of words" · "the thing they charge for"
  • Few-shot promptingIncluding worked examples so the model mimics the pattern"show it examples first" · "teach by demonstration in the prompt"
  • Fine-tuningFurther training a base model on your own examples"train it on my data" · "make it learn my style permanently"
  • Prompt injectionUntrusted input that hijacks the model's instructions"someone talks it into ignoring rules" · "malicious text in the doc"
  • Reasoning modelModel that spends extra hidden compute thinking before replying"the slow smart one" · "the one that thinks first"
  • MCP (Model Context Protocol)Standard for connecting models to tools and data sources"plug my app into Claude" · "the USB port for AI tools"
  • StreamingEmitting tokens progressively so text appears as it generates"the typewriter effect" · "words appearing live"
  • Time to first token (TTFT)Delay before the first character appears"how long before it starts typing" · "the wait before anything shows up"
  • GuardrailsChecks constraining what a model may accept or output"stop it saying bad things" · "the safety fence"
  • EvalsTest suite scoring model outputs against expected behavior"unit tests for prompts" · "how do I know it got better"
  • Prompt cachingReusing a repeated prompt prefix to cut cost and latency"stop paying for the same instructions" · "remember the setup part"
  • Semantic searchRetrieving by meaning similarity instead of exact keywords"search that understands intent" · "finds it even with different words"
  • Hybrid searchCombining keyword and vector retrieval for better recall"keyword plus meaning search" · "use both kinds of search"
  • RerankingSecond-pass model reordering retrieved chunks by relevance"sort the search hits again" · "put the best snippet first"
  • MultimodalModel accepting images, audio, or video alongside text"it can see pictures" · "upload a screenshot and ask"
  • GroundingTying answers to supplied sources with citations"make it cite where it got that" · "answer only from these docs"
  • Human in the loopRequiring person approval before an agent acts"ask me before it does anything" · "approval step"

Deeper in the field

  • Zero-shot prompting Asking directly with no examples provided
  • Top-p / nucleus sampling Probability-mass cutoff controlling word choice diversity
  • Stop sequence String that halts generation when produced
  • Max tokens Hard cap on response length per request
  • Context overflow; truncation Dropping content once the window is exceeded
  • Model router Dispatching each request to the cheapest capable model
  • Quantization Shrinking model weights for cheaper, faster local inference
  • LoRA adapter Small trained layer stacked on a base model
  • Inference Actually running a model to produce output
  • Jailbreak Prompt that bypasses a model's safety training
  • LLM-as-judge Using a model to grade another model's outputs
  • Golden dataset Curated input-output pairs used as the eval benchmark
  • Agentic workflow; agent orchestration Multi-step pipeline coordinating models, tools, branches
  • Subagent Delegated agent handling a scoped task with its own context
  • Knowledge cutoff Date beyond which the model knows nothing
  • Synthetic data Model-generated training or test examples
  • Chain-of-thought prompting Making the model reason step by step before answering
  • Distillation Training a small cheap model to mimic a large one
  • Context rot Quality degrading as the context window fills with noise
  • Open-weights model Model whose parameters you can download and self-host
  • Token budget Allowance of tokens you spend per request or session
  • Prompt template Reusable prompt structure populated with variables at runtime
  • Tokenizer Component that converts text between strings and model tokens
  • Input tokens Tokens supplied to the model as context
  • Output tokens Tokens generated by the model in its response
  • Foundation model General-purpose pretrained model adapted for many downstream tasks
  • Model snapshot Fixed, versioned release of a model with stable behavior
  • Metadata filtering Restricting retrieval using document attributes before similarity ranking
  • Query rewriting Reformulating a request to improve retrieval quality
  • Cosine similarity Common measure of directional closeness between embedding vectors
  • Semantic caching Reusing prior answers for meaningfully similar requests
  • Batch inference Processing many model requests together for lower cost or higher throughput