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Aligning business and IT with AI — the glossary for speaking the same language
Your ERP manages your financial flows, your CRMs follow your customers. Perfect. But where did the PDF contracts go? Negotiation emails? Scanned invoices?
What is AI:
Artificial intelligence (AI) refers to the ability of a machine to perform tasks normally performed by humans: understand language, analyze documents, and make decisions. Today, artificial intelligence relies on models trained on millions of data to learn, adapt and produce contextual responses.
Understanding your vocabulary means starting to master the basics.
Here is a glossary of the 10 AI terms you need to know in order to understand it in business and use your documentary data.
1. Generative artificial intelligence: creating content from your business data
Generative artificial intelligence produces new content: texts, images, code. Unlike traditional AIs that analyze, it creates from scratch from models trained on billions of data.
In business: write meeting summaries, generate customer responses from your documentary database, create automated reports.
Example: An HR person asks “What are the onboarding procedures for a junior salesperson?” ”. The AI analyzes the guides stored in the EDM and generates a summary in 30 seconds.
2. Language models (LLM): the engine of your intelligent assistants
An LLM (Large Language Model) is trained on huge corpora of texts to understand and generate natural language. GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google) or Mistral (French) are LLMs.
Why is it important? LLMs are the “brains” behind your AI assistants and document research tools. They allow you to query your documents in natural language rather than with rigid keywords, via an index.
3. Training data: why your internal documents are strategic
Training data is information that is used to teach a model how to work. Your contracts, invoices, and internal notes are potential training data.
DSI vigilance points:
- Quality: Unstructured or erroneous data = wrong AI
- Security: Who has access? Where are they stored?
- Sovereignty: Model hosted in Europe or in the United States?
Key definition: Training data is to AI what foundations are to a building. A well-structured EDM with consistent metadata is the essential starting point.
4. RAG (Retrieval-Augmented Generation): the AI that responds based on your documents
The RAG allows the AI to search your documents first before responding. Instead of using its general knowledge (sometimes obsolete), the AI queries your database like your GED, retrieves the relevant passages, then generates a response based on your sources.
Key point: Your documents should be organized and indexed. A structured EDM with metadata optimizes relevance. AI does not work miracles on documentary chaos.
Comparative asset: LLM alone vs LLM + RAG
Example: “What is our remote work policy? ” With the RAG, the AI quotes your exact house rules.
5. Embedding: how AI understands the meaning of your documents
An embedding (or vector representation) is a numerical representation of text in vector form. This concept allows AI to measure the similarity between content.
In practice: You are looking for “unpaid invoice” → the AI understands that “outstanding debt” is similar. It's semantic research: meaning takes precedence over exact form.
6. Unstructured data: the blind spot of your IS
Unstructured data is information that does not fit into a table: PDFs, emails, images, videos. This notion is essential to understand where 80% of your business data is hidden (Gartner).
Related terms:
- Dark data: data collected but never analyzed
- Shadow data: data outside official IS (local Excels, personal Dropboxes)
AI is changing the game thanks to NLP and computer vision: extraction of contractual clauses, detection of anomalies, automatic classification.
To exploit this data with AI, organize it in an EDM with:
- A consistent ranking plan
- Relevant metadata (date, author, type)
- High-quality indexing
Without structure, even the best AI will struggle. A well-thought-out EDM is the essential foundation.
7. Machine learning: when machines learn without being programmed
Machine Learning allows systems to learn from data, without explicit programming. You give examples, the model discovers the rules.
Business examples:
- Automatically classify your invoices by supplier
- Detect duplicate documents in your EDM
- Predict the type of document (invoice, contract, purchase order)
- Automatically extract amounts, dates and references
Deep Learning is a sub-category of ML using neural networks to process complex data (images, voices).
8. Prompt: query your documentation like an expert
A prompt is an instruction given to the AI to guide its response. Understanding this concept allows you to effectively interview your GED with AI, without being an expert. You can deploy:
✅ Pre-prompted AI agents: assistants configured by business need
✅ A library of prompts: ready-to-use question templates
Comparative asset: Prompt wave vs structured
9. Hallucination: the risk of an AI disconnected from your sources
A hallucination is a mistake where the AI invents false but credible information. This technical term refers to a major risk of LLMs when they operate without anchoring to verified sources. LLMs work probabilistically: they predict the next word statistically. When they lack information, they “fill in the gaps.”
Famous case: An American lawyer used ChatGPT to write a memoir citing non-existent case law (NYT, May 27, 2023).
How can you reduce hallucinations?
✅ Use the RAG (anchor on your documents)
✅ Require cited sources
✅ Consistently check
10. Fine-tuning: adapting AI to your vocabulary
Fine tuning refines a model to your specific data. A generic LLM doesn't know your internal acronyms or business jargon. Fine tuning teaches him your “corporate language”.
RAG and fine-tuning are complementary:
- The RAG anchors the AI on your documents without changing the template
- Fine-tuning adapts the model to your vocabulary
11. Agent AI — The autonomous AI that works for you
An AI agent allows you to plan and execute actions. He doesn't just answer: he decides the steps and takes action.
What you can do:
✅ Set up a team of agents with different expertise
✅ Your employees don't need to know how to prompt
✅ Have several LLMs work together
✅ Connect your tools via MCP servers
Example: An “HR Documentation Agent” questions your EDM, finds the relevant documents, and suggests: “Do I summarize the procedure, I extract the conditions, or do I create an email ready to send with your voice? ”
The agent adapts his questions and asks for your arbitration if necessary.
Conclusion: From theory to action
These 10 key concepts (+ 1 bonus) now give you a common vocabulary to communicate with your IT teams and your technology providers.
At Efalia, we position EDM as the foundation of your business AI. Thanks to native RAG, your intelligent assistants rely on your real documents — not on outdated generic knowledge.
👉 Discover how to integrate AI into your document management:
Efalia Start IA - Your EDM enhanced by artificial intelligence
