Plain-English definitions for the terms developers and technical buyers encounter while comparing AI coding assistants, agent platforms, RAG infrastructure, LLM API pricing, and developer SaaS alternatives.
24 definitions
AI Coding
A developer tool that helps write, edit, explain, test, or review code with language models and repository context.
Why it matters: Teams should evaluate coding assistants by accepted diffs, review quality, private code controls, and fit with existing workflows.
AI Agents
A platform for orchestrating model calls, tools, memory, approvals, traces, and retries across multi-step AI workflows.
Why it matters: Agent platforms become valuable when automation needs observability, permissions, evaluation, and safe rollback.
AI Agents
A workflow where a model plans or executes several steps, often using tools, retrieved context, validation, and human approval.
Why it matters: Agentic workflows can multiply cost and risk because one user request may trigger many model and tool calls.
AI Agents
A structured request from a model or agent to an external function, API, database, file system, or application action.
Why it matters: Tool calls need permission boundaries, validation, logging, budget controls, and approval gates for risky actions.
AI Agents
A control pattern where a person reviews or approves an AI action before the system performs a risky or irreversible step.
Why it matters: Approval can improve safety, but it also adds cost and latency that should be included in automation ROI.
RAG
Retrieval augmented generation, a pattern where a system retrieves relevant context before asking a model to generate an answer.
Why it matters: RAG quality depends on retrieval, permissions, chunking, citations, and refusal behavior, not only the language model.
RAG
A search backend optimized for storing embeddings and finding semantically similar documents, chunks, products, or records.
Why it matters: The right vector database affects retrieval quality, latency, cost, metadata filtering, and operational complexity.
RAG
A numeric representation of text, code, image, or structured data that lets search systems compare semantic similarity.
Why it matters: Embedding choice affects retrieval quality, storage cost, re-indexing effort, and how well queries match real documents.
RAG
A retrieval method that combines keyword search with vector search to handle exact terms and semantic intent together.
Why it matters: Hybrid search is important for product names, error codes, API methods, legal clauses, and other exact-match queries.
RAG
Filtering retrieval results by fields such as tenant, user role, document type, language, timestamp, or product area.
Why it matters: Weak metadata filtering can return context that is irrelevant, outdated, or not authorized for the current user.
RAG
A second retrieval step that reorders candidate documents or chunks so the most useful context reaches the final model prompt.
Why it matters: Reranking can improve answer quality, but it adds latency and cost that should be tested against real queries.
RAG
The practice of requiring generated answers to rely on retrieved evidence instead of unsupported model memory.
Why it matters: Grounding improves trust only when citations, retrieved chunks, and refusal behavior are tested together.
API Costs
The recurring cost of model API usage, usually driven by input tokens, output tokens, model choice, retries, and workload volume.
Why it matters: API costs should be modeled per feature because a summarizer, support assistant, RAG workflow, and agent have different token shapes.
API Costs
A unit of text sent to a model, including system instructions, user messages, chat history, retrieved context, and tool schemas.
Why it matters: Input tokens can dominate cost when systems repeat long prompts or send too many retrieved chunks.
API Costs
A unit of text generated by a model, including explanations, code, JSON, summaries, answers, or tool arguments.
Why it matters: Long responses and repeated repair attempts can make output tokens a major cost driver.
API Costs
The maximum amount of input and output a model can process in one request, usually measured in tokens.
Why it matters: Large context windows can simplify workflows, but sending repeated long context can raise cost and latency.
API Costs
A strategy that sends different tasks to different models based on complexity, cost, latency, quality, or fallback needs.
Why it matters: Routing can reduce cost when simple tasks use smaller models and high-risk tasks use stronger models.
API Costs
The share of model requests that can reuse cached prompts, context, or intermediate results instead of paying full repeated processing cost.
Why it matters: A higher cache hit rate can reduce LLM API spend for repeated instructions, stable retrieved context, and common workflows.
LLMOps
Tracking prompt changes over time so teams can compare behavior, debug regressions, and roll back unsafe releases.
Why it matters: Prompt versioning turns AI changes into reviewable releases instead of invisible production behavior shifts.
LLMOps
A curated set of prompts, inputs, expected behavior, and edge cases used to test model, prompt, or retrieval changes.
Why it matters: Evaluation datasets catch regressions before a change affects users or customers.
LLMOps
Monitoring and tracing model behavior, prompts, retrieval inputs, tool calls, latency, cost, and user feedback.
Why it matters: LLM observability helps teams diagnose bad answers, cost spikes, schema failures, and prompt regressions.
SaaS Alternatives
A competing software product or architecture that can replace an existing SaaS tool while changing cost, workflow, or control.
Why it matters: Alternatives should be compared by migration cost, reliability, integrations, security, and long-term operations, not only price.
SaaS Alternatives
Dependence on a vendor through proprietary APIs, data formats, saved workflows, integrations, pricing, or team habits.
Why it matters: Lock-in is not always bad, but buyers should know the cost of leaving before a tool becomes critical infrastructure.
SaaS Alternatives
The ability to export data, configuration, logs, users, permissions, traces, documents, or workflows from a platform.
Why it matters: Data portability lowers migration risk and gives teams more negotiating power when pricing or requirements change.
