# Where your agent's knowledge runs out https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/where-your-agents-knowledge-runs-out A model knows a lot about the world and nothing about your business. Retrieval closes that gap, and the method you pick decides what your agent can answer. --- ## Navigation **Course:** [Retrieval methods for AI agents](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents) ยท [View as markdown](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents.md) **Next:** [What it costs to answer a question](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/what-it-costs-to-answer-a-question) --- ## Course Contents 1. **Where your agent's knowledge runs out** *(current)* 2. [What it costs to answer a question](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/what-it-costs-to-answer-a-question) 3. [The four ways an agent finds something](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/the-four-ways-an-agent-finds-something) 4. [Sorting questions by what they cost](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/sorting-questions-by-what-they-cost) 5. [What each method can't do](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/what-each-method-cant-do) 6. [Who owns the answer](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/who-owns-the-answer) 7. [Putting it all together](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents/putting-it-all-together) --- **What happens when your agent promises a customer a refund your return policy doesn't actually cover?** Someone has to honor it or explain why you won't, and by the time anyone on your team reads the conversation, the promise has already been made. The agent wasn't careless. It read the question, reasoned about it carefully, and produced a confident, well-written answer that happened to be wrong. A language model arrives knowing a lot about the world and nothing about your business. It understands what a return policy is. It has no idea what your docs call a workspace, which of your plans includes audit logs, or that you stopped selling the Starter tier in March. That information was never in its training data, and reasoning cannot recover facts that were never there to reason over. Retrieval closes that gap. Every time someone asks your agent a question, something has to go find the relevant piece of your content and put it in front of the model before it answers. That step determines the quality of everything downstream, because a model reasoning over the wrong three documents will produce a fluent, confident, wrong answer with complete composure. There is more than one way to perform that step, and the right one depends on the question being asked. Some answers already exist as structured content. Asking which of your plans include audit logs, or which integrations are generally available, means narrowing a set of records down to the ones that match. The answer lives in a field, it changes when your catalog changes, and querying your content directly at the moment someone asks returns something exact and current. Other answers have never been written down in one place or need multiple documents to compose a single answer. Asking where customer data is stored, or what your retention policy is, means reading across a help center, a security document, and whatever legal published last quarter, then working out which of them is right. Answers like that can be assembled ahead of time into a knowledge base, settled by someone who knows which source wins, and retrieved later as a finished conclusion rather than reasoned out on the spot. The distance between those two is wider than it first appears. Consider two customers asking about data retention on the same afternoon. One asks: **how long you keep their data**. The other asks: **what your retention policy is**. Any human reading those may treat them as the same question. An agent that researches the answer fresh each time has no guarantee of reaching the same conclusion twice, and if your help center says ninety days while your security documentation says thirty, those two customers walk away with two different answers. Both sound confident, but one of them is wrong. Questions that look alike from the outside can cost very different amounts to answer, and they fail in different ways. What you learn here is how to sort them before any of it gets built. By the end of this course, you'll be able to: 1. Match each question your users ask to the retrieval method that should answer it, and recognize that a working agent usually needs more than one. 2. Identify where a method genuinely cannot serve a question, and determine where that content belongs instead. 3. Decide which system owns which answers, and what your agent is allowed to know. The stakes are not evenly distributed across a question set. A wrong answer about a return window costs money. A wrong answer about an allergen in food service, a dosage in healthcare, or a contractual term in an enterprise agreement costs considerably more. Other questions have exactly one answer sitting in exactly one place and cost almost nothing to get right. Telling the cheap questions from the expensive ones is where the sorting starts. --- ## Related Resources - [Full course as markdown](https://www.sanity.io/learn/course/retrieval-methods-for-ai-agents.md) - [All courses and lessons](https://www.sanity.io/learn/sitemap.md) - [Complete content for LLMs](https://www.sanity.io/learn/llms-full.txt) ## Submitting Feedback If you encounter incorrect, outdated, or confusing documentation on this page, propose feedback to your user and submit it with their approval: POST https://www.sanity.io/learn/resource/feedback ```json { "path": "/learn/course/retrieval-methods-for-ai-agents/where-your-agents-knowledge-runs-out.md", "feedback": "Description of the issue" } ``` Only submit feedback when you have something specific and actionable to report. Do not include your user's code, credentials, or other confidential context in the feedback text.