How to Build a Knowledge Base From Your Documents (PDF, Word) in Minutes

Most teams already own the answers their customers, colleagues and partners keep asking for. They are just buried in PDFs, Word files, exported spreadsheets and old HTML pages. The good news: you no longer need a wiki project, a taxonomy workshop or a developer to build a knowledge base from those documents. With retrieval-augmented generation (RAG), you upload the files, the text is indexed, and an AI answers questions by quoting the exact passages it relied on. This guide walks through the whole process, from choosing documents to testing answers, with the pitfalls to avoid along the way.

What is a document-based knowledge base, exactly?

A traditional knowledge base is a set of articles someone writes and maintains by hand: an FAQ, a help center, an internal wiki. A document-based knowledge base works the other way around. You keep your existing documents as the source of truth, and a system makes them searchable and answerable in natural language.

Under the hood, this is what people call RAG. When someone asks a question, the system first retrieves the most relevant passages from your documents, then asks a language model to write an answer using only those passages, with citations. If you want the full picture, read our pillar guide What is RAG?. The short version: the model does not need to have memorized your content, it reads it at question time.

Why build a knowledge base from documents you already have?

The main reason is leverage. Writing documentation is expensive; reusing it is cheap. A contract template library, a set of internal procedures, a product manual or years of consulting notes represent a lot of expertise that is currently only accessible to people who know where to look.

• Faster answers: people ask a question instead of opening ten files and searching with Ctrl+F.
• Consistent answers: everyone gets a response grounded in the same reference documents.
• Verifiable answers: each claim points back to a passage, so readers can check the source.
• Reuse by AI agents: a knowledge base exposed through an API or MCP can be queried by AI assistants and agents (Claude, Cursor, ChatGPT or one you built yourself), not just by humans.
• Expertise worth sharing: if your documents contain real expertise, you can add the base to a library that others query, and be paid when they use it (more on that in how to share your expertise as a knowledge base).

Step 1: Choose the right documents

The quality of the answers depends almost entirely on the quality of what you put in. Before uploading anything, define the scope of the base in one sentence, for example: "Everything our support team needs to answer questions about invoicing" or "French employment rules for small hospitality businesses". A focused base answers better than a catch-all one.

• Keep documents that are current and authoritative. Remove drafts, superseded versions and duplicates, or the AI may quote an outdated rule.
• Prefer documents with real text. A PDF exported from Word or a web page has a text layer; a scanned photocopy often does not.
• Include reference material (procedures, guides, policies, FAQs) rather than conversational noise (email threads, meeting chatter).
• Check that you have the right to use and share each document, especially if the base will be public.

Step 2: Clean and structure your files

You do not need to rewrite anything, but a little preparation goes a long way. RAG systems split documents into passages; each passage should make sense on its own. Clear headings, short paragraphs and explicit terms help the retrieval step find the right passage.

1. Give each file a descriptive title and, ideally, a date or version number inside the document.
2. Use real headings rather than bold lines, so the structure survives extraction.
3. Replace vague references ("see above", "the previous rule") with explicit ones where it matters.
4. Spell out acronyms at least once per document: users will not always ask with the same abbreviation.
5. Convert complex tables into simple ones, or add a sentence summarizing what the table shows.

We cover this in much more depth in how to prepare documents that AI answers well, including templates for procedures and FAQs.

Step 3: Upload and index the documents

This is the step that used to require a developer and now takes a few minutes. Whatever tool you use, indexing follows the same broad sequence: extract the text, split it into passages, and build an index that can be searched quickly at question time.

Indexing approaches differ. Some systems convert passages into vector embeddings and search by semantic similarity; others use full-text search with language-aware stemming; hybrid systems combine both. Each has trade-offs, which we unpack in how a RAG pipeline works under the hood.

How Kopik builds the base for you

On Kopik, creating a base is free. You give it a topic and a document language, then upload files or paste text: PDF (with a text layer, up to 1,500 pages), Word (.docx) and text formats such as .txt, .md, .csv, .tsv, .json, .html and .xml, up to 4 MB per file and 2 million characters per document. The text is split into passages of about 1,200 characters with roughly 200 characters of overlap, cutting on paragraphs first and then sentences, and indexed immediately with PostgreSQL full-text search. A base holds up to 1,000 documents and 20 million characters, and an account can have up to 20 bases.

The document language (English, French, German, Spanish, Italian, Portuguese, Dutch, or Mixed/other) matters more than it looks: it tunes stemming and stop words for full-text search, so that "invoices" matches "invoice" and filler words are ignored. Pick the language your documents are written in, or Mixed/other if they combine several. You can change it later; the base is then re-indexed, which is allowed a few times per hour.

At question time, a small language model expands the question into keywords and synonyms in the documents' language, so a question asked in another language still finds the right passages. The 8 most relevant passages are retrieved (full-text search, no vector database), and a language model writes the answer using only those passages, with numbered citations. If the base does not contain the answer, it says so. You can add or remove documents at any time.

Step 4: Test the answers before you share

Never publish a knowledge base you have not questioned yourself. Write a list of 15 to 30 real questions, the ones your customers or colleagues actually ask, and check each answer against the documents.

• Is the answer correct? Open the cited passages and verify.
• Is it complete? If a key detail is missing, the relevant passage may be poorly worded or split awkwardly.
• Does it admit ignorance? Ask something outside the scope. A good base says it did not find the information rather than inventing it.
• Does phrasing matter? Ask the same question with different words, synonyms or acronyms.
• Are old versions leaking in? If the answer quotes a superseded rule, remove that file.

Step 5: Choose who can access it

Access depends on what the base contains. An internal HR policy base should stay private. A guide to a public regulation can be shared widely. Most tools offer some variation of three levels.

On Kopik, these are exactly the three options, and every base gets its own shareable page. An unlisted base can only be reached through its link, which contains a long random identifier. A private base is more than a drafts folder: you can query it yourself from the website, the REST API or MCP, for free (fair use: 200 questions a day). Public bases appear in the catalogue of knowledge bases, organized by topic (legal, HR and employment, tax and accounting, health, real estate, tech and product, and more). If your documents contain personal or confidential data, keep the base private and check your internal rules before sharing it.

Step 6: Put the knowledge base to work

A knowledge base is only useful if people and tools actually query it. There are three common ways to plug it in.

• Web chat: share the link, and users ask questions in the browser.
• REST API: your own app, website or internal tool sends a question and receives an answer with sources, or just the relevant passages if you want your own model to reason on them.
• MCP: AI assistants and agents (Claude, Cursor, ChatGPT or your own) connect to the base through the Model Context Protocol and consult it when they need to. See how to connect a knowledge base to Claude with MCP.

Kopik supports all three. The API and MCP server use an API key you create in your dashboard; the details are in the developer documentation. On a private base, all of it is free for you, which turns your documents into a RAG backend for your own agents without any infrastructure: see how to use a private knowledge base as a RAG for your AI agents.

Common mistakes when building a knowledge base

• Dumping everything in. More documents do not mean better answers. Contradictory or outdated files degrade retrieval.
• Mixing unrelated topics. A base about tax rules and one about product onboarding should be two bases.
• Ignoring the language. Full-text search relies on language-specific stemming; set the base's document language to match your documents, or choose Mixed/other if they combine several languages.
• Skipping tests. The first real user should not be the one who discovers the base cannot answer the obvious question.
• Never updating. Set a reminder to review the documents when rules, prices or procedures change.
• Expecting the AI to fill gaps. A good RAG system answers from your documents. If the information is not there, the right answer is "not found".

Checklist: your knowledge base in 30 minutes

1. Write the scope of the base in one sentence.
2. Collect the current, authoritative documents; remove duplicates and drafts.
3. Check that PDFs have selectable text; OCR or replace the scanned ones.
4. Add clear titles, headings and versions where missing.
5. Create the base, set its document language and topic, upload the files.
6. Ask 15 to 30 real questions and verify the cited passages.
7. Fix the source documents where answers fall short, then re-test.
8. Choose visibility: private, unlisted or public.
9. Share the link, or connect it through the API or MCP.