1. LLM (Large Language Model)
Large Language Models are trained on large datasets to understand and generate text. In enterprise AI, LLMs deliver value when they are connected securely to business context such as ERP, CRM, and Microsoft 365.
AI DICTIONARY
AI Dictionary for Enterprise Leaders
Learn the core AI concepts behind Copilot, AI agents, RAG, and ERP and CRM automation. Each term is explained with an enterprise lens focused on secure data access, governance, and real workflows across Microsoft 365, Dynamics 365, SAP, and Oracle environments.
2. Transformer Architecture
Transformers process language using attention to understand relationships across long text. This foundation enables modern LLMs to summarize documents and reason over context in enterprise workflows.
3. Prompt Engineering
Prompt engineering is designing instructions so AI produces accurate, useful outputs. In enterprises, it also includes templates, controlled context, and guardrails for consistent, repeatable workflows.
4. Fine Tuning
Fine tuning adapts a general model to a domain by training it on curated data. Many enterprise knowledge scenarios are better served by RAG to keep answers aligned to current approved documents.
5. Embeddings and Vector
Embeddings convert text or images into numeric vectors so systems can measure semantic similarity. This enables enterprise semantic search, classification, and retrieval across policies, tickets, and operational documents.
6. RAG (Retrieval Augmented Generation)
RAG retrieves trusted sources before generating an answer. It improves accuracy, reduces hallucinations, and keeps outputs aligned to approved information with traceability and governance.
7. Tokens
Tokens are the smallest units the model processes. They affect cost, latency, and context limits. Summarization, retrieval, and caching help reduce spend while keeping quality high.
8. Hallucination
Hallucination is when AI produces confident but incorrect information. In enterprises, reduce it through grounded retrieval, validation, approvals for high risk actions, and limiting operations to authorized systems.
9. Zero Shot Learning
Zero shot learning means the model can perform a task without task specific training examples. It supports fast pilots and early validation before deeper integration work.
10. Chain of Thought
Chain of thought refers to multi step reasoning behavior. In enterprise delivery, structured prompts and intermediate checks improve reliability without relying on exposing internal reasoning to end users.
11. Context Window
The context window is how much text the model can consider at once. Enterprise documents often exceed limits, so chunking, retrieval, and summarization keep only relevant context.
12. Temperature and Sovereign AI
Temperature controls how deterministic or creative outputs are. Lower settings fit compliance and finance scenarios. Sovereign AI refers to keeping sensitive enterprise data within controlled boundaries aligned with governance and compliance requirements.
No matching terms found.
How Enterprise AI Fits Together
Enterprise results depend on the system around the model: secure access, the right context, governance, and
integrations that turn outputs into actions.
Model, Context, Governance, Agents, ERP and CRM Actions
LLMs plus embeddings and retrieval, combined with permissions and DLP, then delivered through agent experiences such as Copilot Studio. The result is execution across ERP, CRM, and Microsoft 365 workflows.
Frequently Asked Questions
Answers to clarify architecture choices, data security, and rollout planning.
RAG retrieves the latest approved sources at query time and generates grounded answers using those sources. Fine tuning changes model behavior by training on curated data. For many enterprise knowledge scenarios, RAG is preferred because it stays current and improves traceability without retraining.
Use grounded retrieval, strict prompts, validation rules, approvals for high risk steps, and limit actions to authorized systems. This improves reliability and supports auditability.
Agents connect knowledge to actions. They can create records, trigger workflows, request approvals, and orchestrate tasks across systems like Dynamics 365, SAP, Oracle, and Microsoft 365, based on business rules and governance.
More tokens increase latency and cost. Enterprise optimization often includes summarizing long threads, retrieving only relevant chunks, caching, and using structured templates so you get strong results with fewer tokens.
Let’s turn AI concepts into production results
Understanding AI is step one. Implementing it securely across ERP, CRM, and Microsoft 365 workflows is where measurable value begins.