Expert Kernels (Beta Release)
Introduction
What are Expert Kernels?
For teams building AI applications, Expert Kernels is an LLM-ready knowledge delivery solution. Unlike other knowledge retrieval options on the market, our product provides exactly the knowledge you need to build a fast, knowledge-powered LLM solution.
Without Kernels, teams building LLM-powered applications have limited options to build knowledge into the ecosystem. With Kernels, Expert provides semantically relevant pieces of pages via API, enabling your team to target the parts of your knowledge base that are highly relevant and useful to the query. Your application can use Kernels as the only knowledge source or in combination with other data to construct prompts for an LLM. Ultimately, Kernels enables your generative AI applications to leverage the knowledge already available on your site.
How does Expert Kernels work?
Expert content is available via an API request as kernels. For now, the content in Kernel responses is only from pages with the page restriction set to public. Kernels contain pieces of text from the pages, along with metadata like the page ID, title, and other information. When a natural language search query is entered into the Kernels API, Expert returns the most relevant kernels to that query, regardless of where that content was originally located in the site hierarchy. Kernels automatically contain the most recently published information. All this helps your team create prompts for LLMs that contain the relevant information to build an exceptional solution
What are some benefits of Expert Kernels?
- Faster integration: Use Kernels instead of scraping or parsing a full Expert page in order to build an integration more quickly.
- Improved relevancy: By returning only the most relevant kernels instead of full pages, users are more likely to get pertinent answers to their queries.
- Faster results: Kernels provide answers quickly by eliminating the need to search through full page content.
- Better for AI uses: The ability to retrieve relevant text kernels makes it well-suited for seeding generative AI systems compared to page links.
- Always up-to-date: Kernels auto-update based on source content changes, so the information is always current.
- Context preservation: Kernels contain metadata like page title/URL so the source context of the information is preserved.
What does Beta Release mean?
Until further notice:
- Structures of Kernel responses are subject to change with no warning.
- Structures of Kernel requests are subject to change with no warning.
- Content of Kernel responses for the same query are subject to change with no warning.
- There is no SLA on response time.
Use Case: Pulling knowledge into an LLM application
Use kernels to build prompts out of Expert knowledge into your own LLM solution. An example of an application you could build might work like this:
Compare Kernels to other content retrieval methods
| Knowledge Retrieval API | Query With | Response Is... | Use When |
|---|---|---|---|
| Search Endpoint | User generated keywords | Links to Expert Pages | Users are looking for content on the site, location is unknown but content exists |
| Page Object Endpoint | Page ID | Full content of the page | Content location is known, all the content on the page is desired and you want to display the content elsewhere |
| Kernels Endpoint | User generated natural language question | Parts of pages from around an expert site | Building AI applications that can parse unstructured data, constructing LLM prompts that need factual, up to date information, or building chat bot solutions |
FAQs
What kind of content can be returned as kernels?
Text on pages pages in the main name space. Conditional blocks, reused blocks, and non-text (including tables) won't be returned.
How often are kernels updated?
Kernels are automatically updated based on page changes.
Is there a limit to how many kernels I can request?
The API supports requesting the specific number of kernels per request. The default is 10 and the maximum is 100.
How should kernels be used in my application?
Kernels is optimized for retrieving relevant text for seeding LLMs and building chatbots. Kernels are not formatted for end user display.
How do I access Expert Kernels?
Contact your CSM to get started with kernels.
How do I know if I am a good fit for Expert Kernels?
Kernels was built for technical teams building a knowledge-powered LLM solution. Is this you? Contact your CSM!