Retrieval-Augmented Generation in Production with Haystack

Chapter 1. Introduction to RAG with Haystack

In 2023, a profound transformation occurred. Executives in organizations of all sizes and across all sectors became focused on whether they were capitalizing on the latest advancements in generative AI (GenAI) and if their competitors were pursuing a similar trajectory. Just as the internet revolution and the subsequent smartphone revolution radically reshaped the software development landscape, AI is fueling an analogous paradigm shift. Companies are fundamentally reimagining how customers experience their products.

For example, many organizations are leveraging large language models (LLMs) to unlock data-centric insights into their customers. These LLMs include the OpenAI GPT models, Anthropic’s Claude models, Google Gemini, Meta’s Llama models, Mistral, and more. However, an engine alone cannot propel a vehicle. State-of-the-art LLMs like GPT-4 excel at language-based tasks due to their a priori knowledge, acquired through training on a vast representative corpus of documents (e.g., websites, articles, and books) and tasks involving these documents.

While LLMs demonstrate exceptional out-of-the-box performance, their inherent value is limited. Enterprise use cases involve adapting these LLMs to the organization’s particular data sources and customer workflows. One approach involves feeding the LLM this custom context as part of the input. However, this method presents several challenges, including latency, cost, and model forgetfulness ...