AG Digitalz field guide

Retrieval lets AI answer from your own business knowledge.

Retrieval gives AI access to relevant documents, FAQs, policies, service pages, product details, or sales notes before it writes an answer.

By AG DigitalzPublished Updated
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What it does

Retrieval

Retrieval is useful for businesses with repeated questions, service policies, product catalogs, support docs, or internal knowledge that should guide AI output.

Stack to use

Recommended setup

  • Source documents or FAQs
  • Embeddings or file search
  • Vector database such as Supabase pgvector
  • Retrieval step in backend code
  • Answer prompt with source requirement
  • Human review for public answers
Common mistakes

What to avoid

  • Uploading messy documents.
  • No source visibility.
  • Mixing private and public knowledge.
  • Letting AI answer without retrieved context.
  • Never updating the knowledge base.

Implementation

Build the workflow step by step.

Connect the smallest useful flow first, make ownership clear, then measure it before expanding.

01

Step 1

Collect reliable source documents.

02

Step 2

Remove outdated or sensitive content.

03

Step 3

Chunk the documents into useful sections.

04

Step 4

Create embeddings or file-search records.

05

Step 5

Retrieve the most relevant chunks for each question.

06

Step 6

Ask AI to answer only from retrieved context.

07

Step 7

Show sources or internal references with the answer.

Primary sources

Official documentation behind this guide.

Use these sources to verify capabilities, limits, and setup details before putting a workflow into production.

Official source

OpenAI retrieval/file search

Reference documentation used for this AG Digitalz field guide.

Open source
Official source

Supabase vector docs

Reference documentation used for this AG Digitalz field guide.

Open source