EnterpriseLLM.aiHow it works
From your documents to a trustworthy answer, in six steps
Most AI assistants that answer from a company's own documents follow much the same path, whoever builds them. Engineers call the approach RAG, short for retrieval-augmented generation: the system looks things up first ("retrieval"), then writes the answer ("generation").
Knowing the six steps is enough to ask vendors and your own team the right questions, and to see where projects usually go wrong. The list of steps follows you as you read; tap or click any step to jump straight there.
One picture runs through every step: imagine hiring a meticulous research assistant and giving them the run of your company's files.
Where it starts
It starts with what your company already knows
Most of what your people need to know is already written down somewhere: policies, product manuals, contracts, past proposals, meeting notes, spreadsheets, support tickets. The trouble is finding it. It is spread across shared drives, SharePoint, email and a dozen other systems, often in several versions.
An AI assistant built on your documents does not replace any of that. It reads what is already there, and answers from it.
Think of it as
Imagine hiring a research assistant and, on day one, handing them the keys to every filing cabinet and shared drive in the company.
Why it matters to you
The assistant can only be as good as what it is given. Outdated, duplicated or contradictory documents produce outdated, contradictory answers.
Step 1 of 6
Bring the documents in and prepare them
First the system copies your documents in and reads the text out of every file, including scanned pages, tables and slides. Then it splits each document into short passages of a few paragraphs, because a question is usually answered by one passage, not a whole 80-page manual. Engineers call these passages "chunks".
Each passage is labeled with where it came from, how recent it is, and, most importantly, who is allowed to see it.
Think of it as
Your assistant works through every file, retypes the scanned ones, and makes a note card for each topic. Each card records the source, the date, and a confidentiality stamp.
Why it matters to you
This unglamorous step decides most of the answer quality. And if "who may see this" isn't recorded here, it can't be enforced later.
Step 2 of 6
Build catalogs so any passage can be found in a fraction of a second
Next the system builds catalogs of every passage, usually two kinds. One finds exact words: product codes, names, clause numbers. The other finds meaning, so a question about "time off for a new baby" also finds the "parental leave policy", even though no words match. You may hear this called "vector search" or "semantic search".
Some systems also map how things connect, such as which customer signed which contract, so they can answer questions that span several documents.
Think of it as
Your assistant builds two card indexes. One is alphabetical, like the index at the back of a book. The other is filed by subject, so related cards sit together whatever words they use.
Why it matters to you
Most "the AI couldn't find it" complaints trace back here. Meaning-only search misses exact codes and names; word-only search misses everything phrased differently.
Step 3 of 6
Find the passages that actually answer the question
When someone asks a question, the system searches both catalogs, combines the two lists of results, and then takes a second, more careful look at the top candidates to put the most useful ones first.
Only a handful of passages go forward to the AI, so getting the right ones to the top matters more than finding many.
Think of it as
Asked a question, your assistant pulls cards from both indexes, spreads them on the desk, rereads the most promising ones, and keeps the best five.
Why it matters to you
If the right passage isn't in that handful, even the best AI model can't give the right answer. It will confidently answer from the wrong material.
Step 4 of 6
Write the answer, and show where every point came from
Now the AI model, the same kind of technology as ChatGPT, writes an answer. The difference is that it is instructed to use only the passages it was handed, and to link each point back to the document it came from. Engineers call this "grounding".
When the passages don't contain the answer, a well-built system says "I couldn't find that" instead of guessing.
Think of it as
Your assistant writes a short memo from those five cards only, with a footnote on every claim. If the cards don't cover it, they tell you so.
Why it matters to you
The links are what make the answers checkable. An answer without sources is an answer you have to take on trust.
Step 5 of 6
Check the answers, and keep the catalogs current
Before launch, and regularly after, the system is tested against questions with known answers, so you know how often it is right. Watching what people actually ask shows where answers are missing or weak.
And whenever a document changes, the catalogs are updated, and the old version is retired so it stops being quoted.
Think of it as
You spot-check the assistant's memos against an answer key. When a policy changes, they pull the old card and file the new one, or they'll keep quoting last year's rules.
Why it matters to you
This is the cost most budgets leave out. A system nobody maintains gets quietly worse every month.
Step 6 of 6
Put the answers where people already work
People rarely go looking for a new tool. So the answers are delivered where they already are: Microsoft Teams or Slack, the intranet, your website for customers, inside your other business systems, even a phone line.
The same catalogs and the same rules about who may see what apply everywhere.
Think of it as
Your assistant doesn't sit in one office. They answer in the team chat, at the front desk, on the website and on the phone, from the same card files.
Why it matters to you
Where it shows up decides whether it gets used. And every new channel is another place where access rules have to hold.
The result
People get clear answers they can check
An employee or customer asks a question in their own words and gets a plain answer in seconds, with links to the exact passages it came from, drawn only from documents they are allowed to see.
That is the whole system: the AI writes, but the quality comes from everything before it.
Think of it as
Everyone in the company gets the same diligent assistant: one who has read everything, shows their sources, and never hands anyone a file they aren't cleared to see.
Where the picture stops working
unlike a real assistant, the system doesn't understand your business. It matches patterns very fast. That is why the checking in step 5 matters.