Understanding Knowledge Retrieval and Why It’s Valuable to Businesses

Knowledge retrieval is a search method using AI to better understand search queries and provide conversational answers. When an AI search tool gets the intent behind your question and gives you a summary of answers in natural language, it’s performing knowledge retrieval.

Digital search primarily uses keyword matching to answer queries. Search results are displayed as a list of links to click, one by one. But with knowledge retrieval, you get quicker, more context-aware results created from a summary of sources, all without worrying if you used the right keywords in the search box.

What is knowledge retrieval, and why does it matter?

Knowledge retrieval is a type of AI-powered search that finds relevant results with context-aware semantic reasoning and natural language algorithms. These algorithms can index both structured (like spreadsheets and databases) and unstructured data (like audio, video, or images), meaning they can search a wide array of sources.

Knowledge retrieval is the technology behind retrieval augmented generation (RAG), or when an AI platform presents search results in natural language. RAG combines knowledge retrieval with the text generation features of large language models (LLMs) to produce answers that are both easy to understand and likely to be accurate.

Thanks to RAG — and knowledge retrieval more generally — AI search platforms have become dynamic tools that help users find information in a straightforward, conversational way. This eliminates the need for users to rephrase their keyword-based search terms until they land on the specific ones the search engine understands. Knowledge retrieval with AI tools can also break down information silos by parsing the many ways company information may be stored, such as PDFs, meeting notes, or chat channels.

With knowledge retrieval, more data gets searched more efficiently. As a result, users spend less time looking for data and more time acting on it.

Knowledge retrieval vs. traditional search at a glance

How knowledge retrieval works, step by step

You don’t have to be an AI engineer to understand knowledge retrieval. Here’s a simplified explanation of what goes on behind the scenes.

Step 1: The AI platform is granted access to your data

First, connect your AI tool to the data sources and knowledge bases that employees need to search. Because knowledge retrieval also understands unstructured data, you can connect it to non-traditional repositories of information, like instant messages or call notes. You may also wish to let your AI tool search integrated third-party apps, such as your customer relationship management (CRM) software or customer support tool.

Step 2: Information is indexed for search

Just as search engines such as Google or Bing crawl a website so it can be searched, an AI search tool analyzes its connected information sources. This is known as indexing, and typically involves the algorithm reading metadata, tags, and content categories to understand the information.

AI-powered knowledge retrieval, however, is not limited to data-based information. It also uses contextual reasoning to achieve a nuanced understanding of information.

Step 3: A retrieval model finds search results

Retrieval models are algorithms used to find answers to search queries. Traditional models might match exact keywords or use semantic clues to expand a keyword’s reach. At the more sophisticated end of traditional search are vector search models, which map relationships between similar words on vectors to surface more results.

AI search takes vector search even further by combining vector databases with deep semantic analysis and machine learning. This lets it generate sophisticated, context-rich responses.

Step 4: Results are ranked by relevance

Just like Google ranks search results, knowledge retrieval tools evaluate the relevance of each potential result. They look for many of the same qualities that Google does — such as how recent a source is and whether it’s authoritative — as well as some features that only a machine learning algorithm can properly evaluate. For example, knowledge retrieval engines also assess a result’s relevance as it relates to a specific user’s search behaviors.

How knowledge retrieval works in Slack 

Slack’s enterprise search feature brings knowledge retrieval directly into the work operating system where employees already collaborate. With the support of Slack AI, they get straightforward, AI-generated summaries of enterprise search results in seconds.

After users type their questions into Slack’s search bar, AI-powered workplace search scans messages, channels, and linked apps to find answers. It uses semantic reasoning to understand the context and intent behind user queries and applies similar algorithms to find the exact information needed.

Then, instead of forcing the user to click through a series of links, Slack AI collects information across multiple sources and summarizes it into a few helpful sentences. The algorithm’s internal ranking system refines these summaries according to user behavior signals, such as past searches and links clicked.

Benefits of knowledge retrieval for teams

AI-powered knowledge retrieval improves enterprise searches significantly. It helps users find information from multiple sources and formats almost instantly.

Some benefits your team could see from knowledge retrieval include:

• Reduced repetitive queries. In traditional search, if you don’t phrase a query exactly as the words appear in the searched information, you get poor results and have to try again. By contrast, the contextual reasoning of knowledge retrieval means you’re more likely to get the answer you’re looking for the first time.
• Enhanced efficiency across teams. Disparate teams working on the same projects align by using knowledge retrieval for quick recaps on workflow statuses, tasks, assignments, a project’s next steps, and more.
• Improve onboarding for new hires. When new employees inevitably have questions, knowledge retrieval lets them rapidly find the answers they need without disrupting their workflow.
• Faster access to historical decisions. When someone’s confused about how something is done or why it’s done that way, they may want to review past conversations about the topic. Knowledge retrieval makes doing so simple, even if the information they seek is buried deep in a Slack channel or email exchange.

Enterprise knowledge retrieval use cases

Departments and teams rely on being able to efficiently share and access company knowledge. These are just a few examples of how various departments benefit from knowledge retrieval:

Human resources: policy and process lookups

HR policies can be highly nuanced, and HR professionals are expected to know them inside and out. When an HR professional needs to provide the exact details of a policy during a meeting with an employee, they can use knowledge retrieval to quickly call it up without having to pause the conversation and dig through documentation.

Operations: workflow clarification

Getting workflows wrong can slow or even stop important projects. If an operations manager is unclear about the next step in a workflow and can’t find the notes from the meeting where they hashed it all out, they can ask Slack AI to search meeting transcripts for workflow details.

Engineering: technical discussion recall

When building apps on a tight timeline or managing large enterprise databases, engineers keep track of many technical details. If an engineer encounters a challenge and can’t remember how to get past it, they don’t need to waste time poring through technical documentation. They can use knowledge retrieval to surface critical information instantly.

Sales and service: customer information and historical data

Failing to remember details about a client’s past experience can feel like an affront to them. With knowledge retrieval, your customer-facing teams won’t have to worry about making that misstep. They can ask their AI tool for a quick summary of all past client interactions to instantly know each client’s needs and preferences.

Best practices for designing your knowledge retrieval workflow and processes

When managed properly, knowledge retrieval has the potential to be remarkably easy to use. When not, however, it can be prone to LLM-related hallucinations or security lapses. Follow these best practices to get the most out of a knowledge retrieval tool.

• Define clear knowledge sources. While one strength of knowledge retrieval is the breadth of what it can search, implement clear boundaries around what it is and isn’t allowed to index (such as personal client files). This helps users understand what resources they’re accessing, and it helps you maintain control over your data.
• Monitor AI suggestions for accuracy. Even the best AI tools occasionally hallucinate, which means they offer inaccurate results. Connecting your AI search solution to company data should reduce this. If your knowledge retrieval tool produces a high percentage of inaccuracies, you’ll want to investigate why.
• Set appropriate permissions. Access control and permissions policies become vitally important when using a tool designed to connect to several information sources. Have strong data permissions in place for every system your knowledge retrieval tool accesses.
• Educate teams on search strategies. Users might use one or two keywords, while others may type vague queries that don’t work well with knowledge retrieval. Sharing pointers on how to best phrase queries, such as writing out a complete sentence question, can go a long way toward saving time and getting quality results.

Knowledge retrieval is a competitive advantage 

The pace of business gets faster every day, and your users can’t slow down whenever they need to look for information. They need to get answers instantly, seamlessly, and right in the place where the rest of their work happens.

Slack AI transforms scattered sources of information into accessible answers — all within Slack. With Slack AI’s knowledge retrieval powering your enterprise search, you can feel confident you have a dependable, organized search function your team can rely on.

Knowledge retrieval FAQs