Building a RAG Knowledge Base Your Team Will Actually Use

Why Most RAG Implementations Fail

Retrieval-Augmented Generation (RAG) is one of the most powerful tools in the AI stack. Feed it your documents, ask it questions, get accurate answers grounded in your actual data — no hallucinations, no generic responses.

In theory.

In practice, most RAG implementations fail for one of three reasons:

1. Poor chunking — documents are split in ways that destroy context
2. Weak retrieval — the wrong chunks are returned for a given query
3. No one uses it — the interface is buried or awkward

We've built enough of these to know exactly where each failure mode lives — and how to prevent it.

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Step 1 — Document Preparation

Before anything goes into the vector database, it needs to be clean and well-structured.

What we do:

• Strip headers, footers, and page numbers from PDFs
• Preserve semantic structure — headings indicate topic boundaries
• Keep tables together — never split a table across chunks
• Add metadata to every chunk: source document, section title, date

Chunk size: We typically use 512 tokens with a 64-token overlap. Smaller chunks give more precise retrieval; the overlap ensures context isn't lost at boundaries.

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Step 2 — Embedding + Vector Storage

We use OpenAI text-embedding-3-small for most projects — fast, cheap, and accurate enough for business documents.

For storage, Supabase with pgvector is our default for projects that need to stay within a client's existing infrastructure. Pinecone or Weaviate for larger scale.

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Step 3 — Retrieval Strategy

Naive vector search returns the most similar chunks by cosine distance. It works, but it misses a lot.

We layer two additional strategies on top:

Hybrid search — combine vector similarity with BM25 keyword search. Keyword search catches exact matches (product codes, names, acronyms) that embedding search often misses.

Reranking — after retrieving the top 20 candidates, a cross-encoder reranker (Cohere Rerank or a local model) scores each chunk against the query and returns only the top 5. This dramatically improves precision.

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Step 4 — The Interface

The best RAG system in the world fails if nobody opens it.

We build chat interfaces that:

• Live where your team already works (Slack, Teams, or a simple web app)
• Show source citations so users can verify answers
• Fall back gracefully when the knowledge base doesn't have an answer
• Log queries so you can identify gaps in coverage

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Results We've Seen

A recent deployment for a professional services firm reduced the time to answer internal policy questions from 15 minutes of searching to under 30 seconds — with cited sources the team could verify.

Adoption was at 85% within two weeks because the answers were accurate and the interface was Slack — where the team already spent their day.

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Building a knowledge base for your team? Let's talk →