AI & Software Development Glossary
What is this glossary?
The Code and Trust glossary defines key terms in AI implementation, custom software development, and workflow automation. Each entry includes a direct definition and links to related concepts — helping operations leaders and founders evaluate AI vendors without needing a technical background.
These definitions are written for business decision-makers, not engineers. Every term links to related concepts and, where relevant, to Code and Trust services that implement that technology.
A
AI Agent
An AI agent is an LLM-powered system that autonomously plans, selects tools, executes multi-step tasks, and loops until a goal is achieved -- without requiring step-by-step human instruction. AI agents extend a language model''s capability from answering questions to taking actions: writing code, querying APIs, browsing the web, and updating databases.
Read definition →AI Implementation
AI implementation is the end-to-end process of integrating artificial intelligence into a business's existing workflows, systems, and software -- from identifying high-ROI automation opportunities through deploying production-ready AI systems. Done well, it replaces manual, repetitive processes and can reduce operational labor cost by 30-60% within the first year.
Read definition →AI Readiness Assessment
An AI readiness assessment is a structured evaluation of an organization''s data quality, infrastructure, process maturity, and workforce capability to determine where AI implementation will deliver the highest return and what prerequisites must be addressed first. Organizations that conduct a readiness assessment before AI projects report 2-3x higher project success rates than those that begin implementation without it.
Read definition →AI Workflow Audit
An AI workflow audit is a structured analysis of an organization''s existing business processes to identify which manual workflows are the highest-value candidates for AI automation -- ranked by labor cost, error rate, volume, and implementation complexity. A well-executed AI workflow audit produces an actionable prioritized roadmap, not a generic recommendation to "use AI."
Read definition →AI Workflow Automation
AI workflow automation is the use of artificial intelligence -- including large language models, computer vision, and decision engines -- to execute multi-step business processes that previously required human labor. Unlike rule-based RPA, AI workflow automation handles unstructured inputs such as emails, documents, and voice, reducing manual handling time by up to 80%.
Read definition →AI-Native
AI-native describes software products and companies architected from the ground up with AI as a core capability -- not bolted on after the fact. AI-native applications use LLMs, embeddings, and agent loops as primary product logic rather than as auxiliary features, enabling product experiences that are impossible to replicate by adding AI to a traditional system.
Read definition →API-First
API-first is a software design philosophy where every product capability is exposed through a well-documented API before any user interface is built. API-first systems are consumed by web apps, mobile apps, bots, integrations, and AI agents interchangeably -- enabling 3-5x faster partner integrations and making AI automation straightforward because every business action is already a callable endpoint.
Read definition →ATO (Authority to Operate)
An Authority to Operate (ATO) is the formal approval granted by a federal Authorizing Official that allows a software system to operate within a government environment after completing the NIST Risk Management Framework assessment process. ATOs are required before any federal system goes live and must be continuously maintained -- typically reviewed annually and triggered by significant system changes.
Read definition →Agentic AI
Agentic AI refers to AI systems that operate autonomously over extended task sequences -- planning actions, invoking tools, observing results, and re-planning until a goal is complete without step-by-step human guidance. Unlike single-turn chatbots, agentic systems can execute workflows that span minutes or hours, touching multiple APIs, databases, and services.
Read definition →C
CI/CD (Continuous Integration / Continuous Delivery)
CI/CD is the engineering practice of automatically building, testing, and deploying software every time code is committed to a version control system. Teams with mature CI/CD pipelines deploy to production 200x more frequently with 24x faster incident recovery than teams without automation, according to DORA research -- the most measured indicator of engineering organizational health.
Read definition →CMMC (Cybersecurity Maturity Model Certification)
CMMC (Cybersecurity Maturity Model Certification) is the DoD''s third-party verification program for cybersecurity practices on defense contracts. CMMC Level 2 -- required on most DoD contracts handling Controlled Unclassified Information by 2026 -- mandates independent assessment of all 110 NIST SP 800-171 practices by a Certified Third-Party Assessment Organization (C3PAO).
Read definition →Cleared Developer
A cleared developer is a software engineer who holds an active U.S. government security clearance -- Secret, Top Secret, or TS/SCI -- enabling them to access classified systems, facilities, and data required by certain DoD and intelligence community contracts. Cleared developers bill at 25-45% above uncleared equivalents, with TS/SCI rates reaching $340-$480/hour for senior architects.
Read definition →Containerization
Containerization is the packaging of application code, runtime, libraries, and configuration into a self-contained unit (a container) that runs identically across development, staging, and production environments. Docker containers start in under 2 seconds and use 10x less memory than virtual machines, making them the standard deployment unit for modern cloud-native applications.
Read definition →Custom Software Development
Custom software development is the design and engineering of software built specifically for an organization''s unique requirements -- not configured from an off-the-shelf product. Custom software reduces time-to-market by 3-6 months compared to enterprise SaaS selection and implementation cycles, and delivers exactly the workflow fit that generic platforms cannot match.
Read definition →D
Database Design
Database design is the process of defining the structure, relationships, and constraints of a data store -- tables, schemas, indexes, and normalization rules -- to ensure data integrity, query performance, and scalability. Poor database design is the single most expensive technical mistake to correct after launch, often requiring the zero-downtime migration patterns that add months to a remediation project.
Read definition →Design Sprint
A design sprint is a structured 2-5 day workshop process -- pioneered by Google Ventures -- that compresses months of product discovery into a single week by mapping the problem, sketching solutions, prototyping the highest-potential option, and testing it with real users. Teams that run a design sprint before development begin report 30-50% reduction in rework from misaligned requirements.
Read definition →DevOps
DevOps is the organizational and technical practice of unifying software development and IT operations teams around shared tooling, automation, and accountability for the full software delivery lifecycle -- from code commit through production monitoring. Organizations that adopt DevOps deploy software 46x more frequently and recover from incidents 96x faster than those that keep dev and ops siloed.
Read definition →Discovery Audit
A discovery audit is a structured 2-4 week engagement conducted before any software development begins -- producing a written deliverable that defines scope, identifies technical risks, maps data flows, surfaces compliance gaps, and produces a realistic cost and timeline estimate. Skipping discovery is the single most common cause of software projects going 2-3x over budget.
Read definition →E
Edge Computing
Edge computing is the practice of running application logic at geographically distributed infrastructure nodes close to end users -- rather than in a central cloud region -- to reduce latency and improve reliability. Cloudflare Workers and Vercel Edge Functions execute in under 5ms globally, compared to 50-200ms round-trip from a single-region server.
Read definition →Embeddings
Embeddings are dense numerical vectors -- typically 768 to 3,072 floating-point numbers -- that represent the semantic meaning of a piece of text, image, or other data. Documents with similar meaning produce embeddings that are close together in vector space, enabling AI systems to find relevant content by meaning rather than keyword matching.
Read definition →F
FedRAMP
FedRAMP (Federal Risk and Authorization Management Program) is the U.S. government''s standardized authorization framework for cloud services sold to federal agencies. A FedRAMP Moderate authorization covers 80% of federal civilian use cases, takes 12-24 months to achieve, and costs $500,000-$2,000,000 -- but unlocks a $100 billion+ federal cloud services market with a single reusable authorization.
Read definition →Fine-Tuning
Fine-tuning is the process of further training a pre-trained large language model on a curated dataset of domain-specific examples to adjust its tone, format, or reasoning patterns. A fine-tuned model can match a specialized style with 10-100x fewer tokens at inference time, reducing API cost and latency for high-volume production workloads.
Read definition →G
H
HIPAA Software
HIPAA software is any application that creates, receives, maintains, or transmits Protected Health Information (PHI) and must comply with the HIPAA Security Rule''s administrative, physical, and technical safeguards. Healthcare software companies that handle PHI must sign a Business Associate Agreement (BAA) with covered entities -- violations carry fines of $100-$50,000 per violation up to $1.9 million annually.
Read definition →Hallucination
Hallucination is the failure mode where a large language model generates plausible-sounding but factually incorrect information with apparent confidence. Studies show unmitigated LLMs hallucinate on 15-30% of factual queries, making hallucination mitigation a mandatory engineering requirement -- not a nice-to-have -- for any production AI system that surfaces facts.
Read definition →I
K
L
LLM (Large Language Model)
A large language model (LLM) is a deep-learning model trained on billions of text tokens to predict and generate human-readable language. LLMs such as GPT-4, Claude, and Gemini power chatbots, document summarization, code generation, and AI workflow automation -- and serve as the reasoning engine inside RAG systems and AI agents.
Read definition →Legacy Modernization
Legacy modernization is the process of replacing or incrementally rebuilding outdated software systems -- often monolithic, undocumented, or built on end-of-life frameworks -- with modern, maintainable, and AI-ready architectures. Organizations that modernize legacy systems report 40-60% reductions in maintenance cost and dramatically faster feature delivery.
Read definition →M
MCP (Model Context Protocol)
Model Context Protocol (MCP) is an open standard introduced by Anthropic in 2024 that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP lets an AI agent call database queries, web searches, file systems, and custom APIs using a single protocol instead of bespoke tool integrations for every data source.
Read definition →MVP (Minimum Viable Product)
A minimum viable product (MVP) is the smallest functional version of a product that delivers enough value to real users to generate meaningful feedback and validate core assumptions. Well-scoped MVPs typically take 8-16 weeks to build and cost $25,000-$80,000 -- compared to 12-18 months and $200,000+ for a fully featured first release that may miss the market entirely.
Read definition →Microservices
Microservices is a software architecture pattern where an application is decomposed into small, independently deployable services that each own a single business capability and communicate over APIs or message queues. Netflix and Amazon migrated to microservices to enable thousands of engineers to deploy independently -- teams that adopt microservices report 60-80% reductions in deployment coupling.
Read definition →Model Context (Context Window)
Model context -- also called the context window -- is the maximum amount of text (measured in tokens) that a large language model can process in a single inference call. GPT-4o supports 128,000 tokens; Claude 3.5 supports 200,000 tokens. Longer context windows enable whole-document analysis, multi-turn conversation history, and large-codebase reasoning without chunking.
Read definition →Monolith (Monolithic Architecture)
A monolith is a software application where all features -- UI, business logic, and data access -- are deployed as a single unit. Monoliths are faster to build initially and simpler to operate, making them the right default for most early-stage products. They become a liability when deployment coupling slows team velocity or when different components need to scale independently.
Read definition →N
O
P
Parallel-Run Migration
A parallel-run migration is a legacy system replacement strategy where the new and old systems operate simultaneously for a defined period -- processing the same inputs and comparing outputs before the old system is decommissioned. Parallel runs are the lowest-risk migration approach for mission-critical systems, catching discrepancies before they reach production users.
Read definition →Prompt Engineering
Prompt engineering is the practice of designing, testing, and iterating on the instructions given to a large language model to reliably produce accurate, consistent, and useful outputs. Well-engineered prompts can increase LLM task accuracy by 20-50% compared to naive instructions, often eliminating the need for more expensive fine-tuning.
Read definition →Q
R
S
SOC 2
SOC 2 (System and Organization Controls 2) is an auditing standard developed by the AICPA that evaluates a software company''s controls over security, availability, processing integrity, confidentiality, and privacy. A SOC 2 Type II report -- covering 6-12 months of operating effectiveness -- is increasingly required by enterprise buyers and is a de facto procurement requirement for B2B SaaS vendors.
Read definition →SSR (Server-Side Rendering)
Server-side rendering (SSR) is the technique of generating HTML on the server for each request and sending fully rendered markup to the browser, rather than shipping a JavaScript bundle that renders in the browser. SSR pages achieve Google Core Web Vitals scores 30-50% higher than equivalent client-rendered SPAs, directly improving search ranking and AI crawler indexability.
Read definition →Section 508
Section 508 of the Rehabilitation Act requires all software, websites, and electronic content procured, developed, or used by the U.S. federal government to meet accessibility standards equivalent to WCAG 2.0 Level AA. Non-compliance can disqualify a product from federal procurement and expose agencies to civil rights complaints under the Architectural Barriers Act.
Read definition →Serverless
Serverless is a cloud execution model where the infrastructure provider automatically provisions, scales, and manages the compute resources needed to run application code -- developers deploy functions or containers without managing servers. Serverless reduces infrastructure operations cost by 40-80% for event-driven and variable-load workloads, eliminating idle capacity charges.
Read definition →Software Project Takeover
A software project takeover is the structured handoff of an in-progress or stalled software project from one development team to another -- including codebase audit, knowledge transfer, risk assessment, and a defined plan to resume or recover delivery. Project takeovers are warranted when a founding team departs, a vendor relationship breaks down, or a project stalls for more than 60 days.
Read definition →Staff Augmentation
Staff augmentation is a flexible engagement model in which a company supplements its in-house team with external engineers, designers, or specialists who work under the client''s direction -- without the overhead of full-time hiring. Onshore staff augmentation typically runs $100-$200 per hour and avoids the 3-6 month delay and $20,000-$40,000 cost of a senior engineering hire.
Read definition →T
V
W
Z
50 terms defined
Ready to put these concepts to work?
Code and Trust translates AI concepts into working implementations. Schedule an audit and we'll identify exactly where these technologies create ROI in your operation.
Schedule AI Audit →