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ContextGuide, described by Akanksha Sharma, puts a retrieval step between a user’s question and an AI-generated answer: Question → Context → Answer. The agent first looks up relevant material in a structured knowledge base, then uses that context to respond. The idea addresses a familiar problem: an AI can sound confident about a technical question even when its answer lacks the details found in the documentation.
How ContextGuide is meant to work
Sharma’s DEV Community post describes a flow in which the agent interprets a question, retrieves relevant documentation, guides, or references from a knowledge base, reasons over that material, and returns an answer with sources. Instead of relying only on what the model already knows, the design gives it somewhere useful to look before it responds.
The post divides the work among three components: Sanity organizes the knowledge, Sanity Context makes the content queryable, and MCP connects the agent to the retrieved context. MCP, or Model Context Protocol, is the connection mechanism in this design—not the agent itself.
For example, a user might ask, “Which authentication method should I use here?” The intended process is to find relevant material first and base the response on it, rather than generate a general answer without consulting the project’s own guidance.
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What Sanity Context does—and does not do
Sanity documents Context as a hosted, read-only MCP server that gives agents structured access to content from a live dataset or a Knowledge Base. A builder still needs to provide an MCP-capable AI harness to run the agent and manage its answer flow. Context supplies access to information; it does not run the agent loop or write changes back to the dataset. Sanity Context documentation.
Sanity documents two ways to retrieve content:
- GROQ mode: queries a dataset when a request arrives. This fits content that is structured and suited to live queries.
- Knowledge Base mode: retrieves from an index prepared ahead of time. Knowledge Bases can draw on datasets, websites, and files; Sanity currently describes this feature as an opt-in beta. This mode may suit knowledge spread across different sources or formats. Sanity Context documentation and Sanity Knowledge Base documentation.
The choice is about how the source material is organized and retrieved, not a guarantee that one mode will produce more accurate answers. GROQ queries live structured data; a Knowledge Base serves a prebuilt index.
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Handling sources that disagree
Sharma imagines a case where two sources recommend different authentication methods. Her proposed behavior is for the agent to acknowledge the disagreement and show what each source says, rather than confidently choosing one. As she puts it, “Sometimes the honest answer isn’t: ‘Here’s the answer.’ Sometimes it’s: ‘Here’s what the sources say and here’s where they disagree.’”
That is a design goal described in the post, not a reported test or a detailed conflict-resolution algorithm. A retrieval step can expose relevant sources, but the article does not establish how ContextGuide ranks conflicting material or decides which source takes precedence.
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What the post establishes about results
The post explains the concept and its component roles, but reports no accuracy rate, benchmark, usage figure, or other measured outcome. Sanity’s documentation verifies the capabilities and limitations of the underlying Context service; it does not verify that ContextGuide was implemented, tested, or shown to improve answer accuracy.
The practical takeaway is narrower and still useful: retrieving relevant knowledge before answering gives an agent material to ground a response in. Whether that makes a particular answer correct depends on the sources retrieved and how the agent uses them.
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