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What a chatbot knowledge base does
A knowledge base is the external information a chatbot can consult when responding. In a RAG system, a retriever searches that collection for passages relevant to a question, then supplies those passages to a language model as context for generating an answer. This can ground responses in an organization’s documentation or other proprietary material without relying only on what the model learned during training. See AWS’s overview of RAG systems and managed or custom approaches.
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RAG does not make an answer automatically true. A system can retrieve the wrong passage, miss the relevant one, or generate a response that misstates its evidence. Treat content preparation, retrieval, citations, evaluation, and maintenance as parts of one system.
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Build the knowledge base step by step
1. Define the questions and authoritative sources
Start with the questions the chatbot is meant to answer: for example, customer-support questions about returns, an internal team’s policy questions, or documentation questions about a product. For each subject, identify the authoritative source and who is responsible for keeping it accurate. Prefer approved, maintained material over an indiscriminate dump of every available file.
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Record each source’s identity and version, and decide how updates will be noticed. That makes it possible to trace an answer back to its origin and replace stale material when the source changes. The GIZ 2025 guide to chatbots for service delivery discusses document processing, metadata, and chunking considerations relevant to building such a collection.
2. Extract documents without losing their meaning
Convert source files into content the retrieval system can index, while preserving context that makes a passage understandable. Headings, section relationships, tables, and page information can all matter. A table flattened into disconnected text, for example, may no longer make clear which value belongs to which row or column.
Plan for the formats actually in the collection, such as PDF, DOCX, HTML, and XML. Scanned or image-based PDFs may require text extraction before their contents can be searched. Inspect converted output rather than assuming that a successful file import means the information was extracted correctly. The GIZ guide identifies document structure and extraction as processing considerations.
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3. Split content into retrievable passages and attach metadata
Long documents usually need to be divided into smaller units that retrieval can return with enough context to answer a question. Split at meaningful boundaries where possible, and retain a link from each passage to its document and useful attributes such as section, page, and version. Metadata can help identify where information came from and distinguish relevant material when several passages use similar terms.
There is no universal best chunk size. GIZ gives examples of small chunks of about 100–200 words, medium chunks of about 400–1,000 words, and large chunks of around 5,000 words. These are implementation examples in its 2025 guide, not measured optima or rules for every corpus. Test candidate sizes against the documents and user questions your chatbot must handle. Very short passages can lose context; very long ones can bring in material that is less focused on the question.
4. Choose a managed service or a custom RAG stack
A managed knowledge-base service can supply ingestion and retrieval components. A custom RAG system lets a team assemble its own document processing, retrieval, storage, and generation components. AWS describes both managed Amazon Bedrock Knowledge Bases and custom RAG as options in its RAG systems documentation; neither is established as the universal winner.
| Decision area | What to compare |
|---|---|
| Document control | How much control the team needs over extraction, chunking, metadata, retrieval, and reranking. |
| Operations | Which components the team will operate, update, and troubleshoot itself. |
| Evidence and evaluation | Whether the approach can expose source references and support repeatable retrieval and generation tests. |
| Data handling | Whether the specific service’s retention, access, region, and data-use terms fit the material being indexed. |
| Corpus fit | Whether the collection contains scanned files, complex tables, or other material needing specialized extraction. |
A vector store is one common component in a RAG system, not a complete knowledge-base strategy by itself. Choose processing and retrieval components based on the corpus, operational requirements, and measured results. The GIZ guide lists example technologies, not a ranking or endorsement.
5. Retrieve evidence and show its source
For each question, retrieve passages that support a response and provide them to the language model as context. Return citations with the answer and make source details inspectable, so users or operators can check what the chatbot relied on. AWS documents a Bedrock retrieve-and-generate flow that returns citations to original source data and supports inspecting source chunks; its documentation states, “The responses are returned with citations to the original source data.” See Query a knowledge base and generate responses based off the retrieved data.
A citation helps a reader inspect evidence; it does not prove that the answer accurately represents it. AWS also documents optional reranking, which can change the relevance order of retrieved chunks. Whether reranking helps should be assessed against the system’s questions and sources rather than assumed.
6. Evaluate retrieval and answer generation separately
Build a test set from representative user questions, including varied wording and difficult cases. Include questions with known supporting passages and expected answers, as well as questions the source collection cannot answer. Then assess two distinct stages:
- Retrieval: Did the system find useful supporting evidence?
- Generation: Did the response use that evidence correctly, cite it appropriately, and handle missing evidence responsibly?
Useful evaluation criteria include answer usefulness, citation correctness, and whether the chatbot recognizes when the indexed material does not contain an answer. No universal score threshold is established by the cited vendor documentation, so teams need a rubric suited to their use case.
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For Amazon Bedrock evaluation jobs, AWS documentation specifies that the prompt dataset is stored in S3 as JSONL, with a maximum of 1,000 prompts per evaluation job. Retrieve-and-generate evaluation supports up to five turns per conversation; retrieve-only evaluation is single-turn. These are limits of that service’s documented evaluation workflow and may change. Details are in Create a prompt dataset for a RAG evaluation in Amazon Bedrock.
7. Maintain sources and rerun tests
Assign owners to the material, refresh the knowledge base when authoritative documents change, and rerun the evaluation set after meaningful source or pipeline updates. Preserve version information so the team can diagnose whether a poor answer came from missing or stale content, extraction, chunking, retrieval, or generation. Treat the chatbot’s sources and evaluation set as maintained product components, not a one-time upload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose an approach that fits your content and team
There is no single best architecture for every chatbot. Use a managed service when its ingestion, retrieval, evaluation, and evidence features meet the requirements and reduce work the team does not need to own. A custom stack is an option when the team needs control over processing, storage, or retrieval components and can operate them. In either case, compare actual performance on representative questions and verify the exact service’s data-handling terms for the content being indexed.
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Frequently Asked Questions
What is the difference between a chatbot knowledge base and RAG?
The knowledge base is the external collection of information. RAG is an approach that retrieves relevant material from a collection and supplies it to a language model so the model can answer using that context.
What is the best chunk size for a chatbot knowledge base?
No universal best size is established. GIZ’s 2025 guide gives examples ranging from about 100–200 words for small chunks to around 5,000 words for large ones; test alternatives against your own source material and representative questions.
Do citations guarantee that a chatbot answer is correct?
No. Citations make supporting source material inspectable, but retrieval and answer generation still need evaluation. Check that the cited passage supports the response.
How should a chatbot respond when its sources do not answer a question?
Include unanswered questions in the evaluation set and assess whether the system handles missing evidence appropriately instead of presenting an unsupported response as fact.
Can a knowledge base include scanned PDFs and tables?
Yes, but extraction must preserve or recover their usable content. Scanned PDFs may need text extraction, and tables should retain enough row, column, and heading context to remain interpretable after processing.
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