What is a Retrieval Augmentation Pipeline?
A system that combines retrieval and generation processes to enhance AI model outputs with relevant knowledge.
More about Retrieval Augmentation Pipeline:
Retrieval Augmentation Pipeline integrates retrieval systems and generative models to produce knowledge-grounded outputs. The pipeline retrieves relevant information from sources like vector databases or knowledge graphs and passes it to a generative model to produce accurate and contextually rich responses.
This approach is widely used in frameworks like retrieval-augmented generation (RAG) and context-aware generation, ensuring responses are factual and relevant.
Frequently Asked Questions
How does a retrieval augmentation pipeline improve AI outputs?
It ensures responses are grounded in reliable information, reducing hallucinations and improving factual accuracy.
What components are commonly part of a retrieval augmentation pipeline?
Key components include retrieval models, embeddings, and generative AI models.
From the blog
GPT-5 vs Claude 4.5: Which AI Is Better for Customer Service Chatbots?
Compare GPT-5 and Claude 4.5 for AI customer service chatbots. Find out which model offers faster, more reliable, and more natural support, and see how each matches your brand’s tone, safety, and performance needs.
Herman Schutte
Founder
Custom model training and fine-tuning for GPT-3.5 Turbo
Today OpenAI announced that businesses and developers can now fine-tune GPT-3.5 Turbo using their own data. Find out how you can create a custom tuned model trained on your own data.
Herman Schutte
Founder