From 0ea4e89368ade381e634e91f491ee44f46fe172a Mon Sep 17 00:00:00 2001 From: Luyao Zhang <83757403+Luyao-Zhang-1@users.noreply.github.com> Date: Wed, 28 Jan 2026 12:17:35 +0100 Subject: [PATCH] Small typo fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Haven't contributed to docs for a while. I read this doc this morning as I wanted to understand the topic. I learned so much (Thank you!) I also fixed a typo and made a small style tweak. Feeling great to make a PR again!😊 --- content/en/docs/marketplace/genai/concepts/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/en/docs/marketplace/genai/concepts/_index.md b/content/en/docs/marketplace/genai/concepts/_index.md index 3b7f479019c..a92df9689d0 100644 --- a/content/en/docs/marketplace/genai/concepts/_index.md +++ b/content/en/docs/marketplace/genai/concepts/_index.md @@ -88,7 +88,7 @@ Often, you can use prompt engineering, RAG, and ReAct to build your use case and ## Prompt Engineering {#prompt-engineering} -Prompt engineering is the activity of designing the input text that will be send to the LLM. This typically contains input from the end-user, enriched with instructions from the developer / administrator. A prompt typically contains: +Prompt engineering is the activity of designing the input text that will be sent to the LLM. This typically contains input from the end-user, enriched with instructions from the developer / administrator. A prompt typically contains: * instructions on what the model should do * context and information that the model needs to follow the instructions @@ -122,7 +122,7 @@ For example, Amazon Bedrock has the concept of [knowledge bases for Amazon Bedro ### PgVector Knowledge Base {#pgvectorknowledgebase} -If your chosen architecture doesn't have fully-integrated RAG capabilities, or if you want tighter control of the RAG process, you can create and use your own knowledge base. +If your chosen architecture does not have fully-integrated RAG capabilities, or if you want tighter control of the RAG process, you can create and use your own knowledge base. In this case you will have to index and store your knowledge yourself, and index your input data in order to retrieve the information with which you want to augment your prompt. For this you can use the [PgVector Knowledge Base module](/appstore/modules/genai/pgvector/) in combination with an embeddings model, to maintain and use your knowledge base.