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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →ContextGuide is an AI agent concept that retrieves relevant knowledge before generating a response. Its flow is simple: a user asks a question, the agent checks a knowledge source, and then answers with context. The idea addresses a familiar problem: a fluent technical answer can sound plausible but prove incomplete when checked against documentation.
How ContextGuide is meant to work
In Akanksha Sharma’s DEV Community article, ContextGuide separates answering into a question, a retrieval step, and a response. The agent first interprets a question such as “Which authentication method should I use here?” It then looks through a knowledge base of documentation, guides, and references, reasons over the retrieved material, and returns an answer with sources.
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The parts have distinct jobs: Sanity organizes the content, Sanity Context makes it queryable, and MCP connects that retrieved context to an AI agent. The key design choice is to put retrieval between the question and the answer, rather than relying only on what the model already knows.
Sharma sums up the principle this way: “Don’t just ask the AI what it knows. Give it somewhere useful to look.”
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What Sanity Context provides
Sanity documents Sanity Context as a hosted, read-only Model Context Protocol (MCP) server. It gives agents structured access to content from a live dataset or a Knowledge Base; it does not run the agent loop or write changes back to the dataset. The builder must provide an MCP-capable AI harness. Sanity’s documentation describes the service and its setup.
Sanity documents two retrieval modes:
- GROQ mode: queries a dataset at request time. This is the relevant option when information is organized in a Sanity dataset and needs to be queried live.
- Knowledge Base mode: retrieves from an index built ahead of time. Knowledge Bases can draw on datasets, websites, and files. Sanity currently describes this feature as opt-in beta.
These are underlying Context capabilities, not proof that ContextGuide itself was implemented or tested with either mode. The DEV article presents the project’s intended flow; it does not establish a working deployment.
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Handling sources that disagree
The article imagines documentation sources that recommend different authentication methods. Its proposed response is for the agent to identify the disagreement and show what each source says instead of confidently choosing one. Sharma writes: “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 intention, not a reported test or a documented conflict-resolution algorithm. The article does not explain how the agent would rank sources, decide which guidance is current, or resolve contradictory instructions. Those behaviors would need to be designed and evaluated in an implementation.
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What the article does—and does not—show
ContextGuide’s central idea is retrieval before generation, with sources made visible in the resulting answer. The article does not report an accuracy rate, benchmark, usage figure, or other measured result, so it does not show that this approach improved answer accuracy. Sanity’s documentation verifies the capabilities of Context, not the implementation or outcomes of this specific project.
For someone building a similar system, the practical starting point is to decide where the knowledge lives: structured content that should be queried at request time points toward GROQ mode; information spread across prose, websites, and files may fit a prebuilt Knowledge Base. That choice alone does not guarantee a reliable answer—the agent still needs to retrieve relevant material, expose its sources, and handle gaps or conflicts carefully.
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