AI & software development glossary
Plain-English definitions of the AI and software terms our clients ask about most. Written by the Laxaar team, kept honest and current.
AI Agent
An AI agent is a system that uses a language model to plan and take multi-step actions toward a goal, calling tools and APIs and remembering context along the way. Unlike a chatbot, it does work rather than just answering a single question.
AI Agent Development →Agentic AI
Agentic AI is software where models act with autonomy: they decide steps, use tools, and adapt based on results, rather than following a fixed script. It's the pattern behind copilots and autonomous workflows.
AI Agents expertise →Generative AI
Generative AI is a class of models that create new content (text, code, images, audio) from a prompt. In products it powers assistants, content generation, and natural-language interfaces.
Generative AI Development →RAG (Retrieval-Augmented Generation)
RAG is a technique that grounds a language model in your own data by retrieving relevant documents at query time and feeding them to the model. It's the most common way to cut hallucination and keep answers current without retraining.
AI Data Pipelines →Fine-Tuning
Fine-tuning is further-training a base model on your own examples so it adopts a style, format, or task. It's useful when prompting and RAG aren't enough, though it costs more to build and maintain.
AI Development →LLM (Large Language Model)
An LLM is a model trained on large text corpora to predict and generate language. It's the engine behind assistants, agents, and most generative-AI features.
AI Development →MCP (Model Context Protocol)
MCP is an open standard for connecting AI models to tools and data sources through a consistent interface. It lets an agent use external systems without bespoke glue for each one.
AI Agent Development →Vector Database
A vector database stores embeddings (numeric representations of text or images) and finds the most similar ones fast. It's the retrieval layer most RAG systems and semantic search features rely on.
AI Data Pipelines →AI Automation
AI automation is using AI to complete tasks that used to need a person, like triaging support tickets or processing documents. It differs from classic RPA by handling unstructured input and judgment, not just fixed rules.
AI Automation Services →MVP (Minimum Viable Product)
An MVP is the smallest version of a product that delivers real value and lets you learn from actual users. Built well, it becomes the foundation of the full product rather than a throwaway.
MVP Development →Multi-Tenant SaaS
Multi-tenant SaaS is an architecture where one application instance serves many customer accounts with isolated data. It's the efficient default for most SaaS, versus single-tenant setups used for strict isolation.
SaaS Development →Product Engineering
Product engineering is building software as an evolving product, owning discovery, design, delivery, and iteration, rather than completing a fixed spec and walking away.
Custom Software Development →Evaluation Harness
An evaluation harness is a test suite for AI systems that scores outputs against expected behaviour. It's how you tell whether an agent or model is actually reliable before and after it ships.
AI Agent Development →CI/CD
CI/CD is the practice of automatically building, testing, and deploying code so releases are fast and repeatable. It removes manual steps and the errors that come with them.
Cloud Deployments →Take your business to the next level.
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