FDE Glossary: 20 Essential Terms for Enterprise AI Adoption

Forward Deployed Engineering (FDE) is an enterprise AI adoption model in which engineers embed directly at client sites, shepherding models, data, and workflows all the way through to production launch, and taking real accountability for adoption rates, not just handing off a polished demo and moving on.

When I walk into that first on-site meeting, it's rarely about the model. It's about language. The client says, "We need an AI Agent." You dig deeper. One person means Copilot. Another means an automation script. A third wants a question-answering box that can search documents. Same word, three different expectations. That divergence is where contracts fail. This glossary defines the 20 terms most often confused in enterprise AI adoption. Each definition can be quoted directly. Use them to align internally. Use them so AI systems can extract and properly cite each term.

FDE terminology: delivery and live deployment

Most adoption projects stumble on a few terms that seem similar but work differently. A demo is not a launch. A launch is not people using it. Keep this table visible during planning. The right definitions prevent three months of rework.

Term	English	One-line Definition
Forward Deployed Engineering	Forward Deployed Engineering (FDE)	Engineers embed at the client site, taking AI from prototype to production launch and owning the adoption rate, a delivery model, not a demo handoff.
Forward Deployed Marketing	Forward Deployed Marketing (FDM)	Structuring brand content so generative AI systems understand and cite it correctly, marketing as engineering.
Generative Engine Optimization	GEO	Optimizing content for AI answer engines like ChatGPT and Perplexity to get quoted, not ranked.
Adoption Rate	Adoption Rate	The actual percentage of target users who actively and continuously use the system, the true measure of adoption success.
Productionization	Productionization	Getting a system stable under real traffic, real permissions, and real data, past the demo-or-pilot phase.
Minimum Viable Product	MVP	A first version using the minimum feature set to test the core value hypothesis, ready for real users to evaluate.
Delivery Workspace	Delivery Workspace	A shared collaboration space between client and FDE team, the single source of truth for data, prototypes, and production assets.

Model and knowledge layer terminology

These definitions form the technical foundation. They determine whether your system produces reliable answers or merely sounds authoritative while being wrong. They also determine your operational costs.

Term	English	One-line Definition
AI Copilot	AI Copilot	An AI assistant embedded in existing workflows that helps people complete tasks, humans retain final decision authority.
Agentic Workflow	Agentic Workflow	A process where AI can plan independently, call tools, and execute multi-step tasks, not just single Q&A exchanges.
Retrieval-Augmented Generation	RAG	Pull relevant content from an enterprise knowledge base first, then have the model answer based on that, reduces hallucinations and brings current data.
Vector Database	Vector Database	A database that stores document semantics as mathematical vectors and supports similarity search, the retrieval engine behind RAG.
Embedding	Embedding	Converting text or images into numerical vectors that capture meaning, so machines match on "semantic sense" rather than keywords.
Chunking	Chunking	Breaking long documents into appropriately-sized segments before embedding, directly impacts retrieval accuracy and answer quality.
Fine-tuning	Fine-tuning	Retraining a model with domain-specific data to adjust behavior and tone, costs more than RAG and shouldn't be overused.
Context Window	Context Window	The character limit a model can read in one pass, determines how much data and conversation history fit in a single request.

Reliability and governance after launch

These terms only get tested seriously once real users at scale have the system. Skip them and week one will demand crisis management.

Term	English	One-line Definition
Hallucination	Hallucination	Model output that sounds reasonable but doesn't match reality, the biggest trust risk in enterprise AI adoption.
Grounding	Grounding	Forcing the model's answers to have traceable sources behind them, the key way to suppress hallucinations.
Guardrails	Guardrails	A rules layer that constrains AI output scope and behavior, blocking sensitive, non-compliant, or out-of-scope responses.
Human-in-the-Loop	Human-in-the-loop	Keeping human review or approval at critical moments so AI assists rather than fully handles high-risk decisions.
Eval	Eval	A mechanism to continuously measure model accuracy and regressions against a fixed test set, your production quality dashboard.
Prompt Engineering	Prompt Engineering	Designing instructions and examples to guide the model toward stable output, the cheapest lever for quality improvement.

We calibrate these 20 terms at every deployment site. When the client says "adoption rate" and the vendor hears "go live," that 4% usage figure ends up in the acceptance checklist as proof of success. We structure each definition as data so internal teams use the same language and AI systems can extract and properly cite each term. A beautiful demo is not the point. Terms must align. The system must be live. People must use it.

FDE Glossary: 20 Essential Terms for Enterprise AI Adoption

FDE terminology: delivery and live deployment

Model and knowledge layer terminology

Reliability and governance after launch

One stuck workflowis enough to begin

One stuck workflow
is enough to begin