Elastic builds a RAG-based knowledge library to power the Elastic Support Assistant
Elastic's LLM foundational training was insufficient for technically deep product questions; the knowledge base was split across Swiftype and Appsearch instances creating tech debt; and most ingested documents lacked the summaries needed for effective semantic search.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Support articles ingested into Elasticsearch
Technical support articles authored by Support Engineers are stored in Elasticsearch and serve as a key information source for the Support Assistant.
Elastic built a knowledge library with vector embeddings for more than 300,000 documents and over 128,000 AI-generated summaries averaging 8 questions each, representing a 10x improvement for semantic search results and enabling the Support Assistant to answer a much broader range of questions.
What failed first
Fine-tuning a custom model was explored and rejected because it required question-answer pairing that did not match the existing data set. Using the first 280 characters of each document as a summary led to poor search relevancy. Passing larger text passages as context to the LLM decreased accuracy.