How retrieval works, briefly
A model does not read your documents when it answers. Before that, the documents are prepared: loaded from their source, split into chunks, turned into vectors by an embedding model and stored in a vector store. At question time, the agent searches the store, pulls back the chunks that look most relevant and uses them as context for the answer. This is retrieval-augmented generation, usually shortened to RAG.
In Cadrix, a knowledge base is a named virtual database that follows those four stages: sources, chunking, embeddings and vector store. Workflows point at the knowledge base instead of at loose files.
Failure one: the text never made it in
The most common problem is also the least visible. A loader reads a file, produces little or nothing, and the knowledge base looks fine from the outside. Scanned PDFs are the classic example: they are pictures of pages, so plain text extraction returns almost nothing. Tables, headers and multi-column layouts can come out scrambled for similar reasons.
The fix is to look at what was extracted before you embed it. For image-heavy PDFs, an extraction mode that reads the pages as images may be needed instead of text extraction. Check the loader options in the app for what is available.
Failure two: chunks that make no sense alone
Chunks are what the agent actually sees. If a chunk cuts a procedure in half, or separates a table from its heading, the retrieved text is misleading even when it is technically relevant. Chunks that are too large dilute the match; chunks that are too small lose context.
There is no universal chunk size. The practical approach is to preview how a splitter cuts a representative document, read a handful of chunks as if you were the model and adjust until they stand on their own.
Failure three: the content went stale
A knowledge base is a snapshot. A policy changes, a price list is replaced, a procedure is retired, and the store keeps answering from the old version with full confidence. Treat freshness as a process: decide who owns each source, how often it is refreshed and how you will know that an upsert actually happened.
It also helps to know what depends on a store before you change it. Cadrix shows how many workflows use a knowledge base, and keeps a history of upserts, so you can see what changed and when.
Failure four: the wrong passage wins
Retrieval is a similarity search, not understanding. A question phrased very differently from the source, an acronym, a product code or a negation can all pull back the wrong chunk. Using the same embedding model for preparing and querying is a basic requirement, and changing the model means re-embedding the content.
The way to find these cases is to test with real questions, including awkward ones, and to read what comes back. A query against the vector store that returns the chunks it found is the quickest check, before an agent is involved at all.
Failure five: the agent answers anyway
Even with the right chunk retrieved, the model might ignore it, mix it with its own memory or answer when nothing relevant was found. Instructions help: tell the agent to answer only from the provided material, and to say so when the material does not cover the question. Then verify it by trying questions your knowledge base cannot answer and seeing what happens.
For anything consequential, pair retrieval with a person. A support reply that cites a policy can still go through an approval step before it is sent.
Do not forget who can see it
A knowledge base can expose whatever it contains to anyone who can use the workflow that reads it. Keep sensitive material in separate stores attached only to the workflows and roles that should reach it, and rely on workspace roles and permissions to control who can build against it.
A short inspection routine
- Load a representative source and preview the extracted text and the chunks.
- Upsert, then query the store with five real questions and read the returned chunks.
- Attach the store to an agent and run the same questions through a test run.
- Open the run trace and confirm that the retrieval step returned what you expected.
- Write down who refreshes the source and how often.
For the product side of this, see Cadrix Knowledge.
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