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Product Knowledge for AI-native teams. The platform where product teams gather to build great software, together

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AI is speeding up the wrong part of software

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<p dir="ltr">AI generates almost-right requirements because it relies on patterns from how requirements usually look, not your product's actual terminology, decisions, and rules. The result is a spec that sounds correct but encodes the wrong meaning.</p>
<p dir="ltr">Almost right is worse than wrong here. A wrong requirement gets flagged; an almost-right one gets approved, built, and discovered weeks later when it doesn't behave the way anyone assumed. And because the output is well-formed and fluent, it often passes review even when the underlying assumptions are wrong.</p>

<p dir="ltr">A concrete case: an AI writes "users can archive projects" when your product actually has a concept called "freeze workstreams" &ndash; a different term, with different rules about who can do it and what stays visible afterward. The sentence reads fine. It's wrong in three ways at once &ndash; the wrong word, the wrong rule, and a meaning your team would never have written.</p>
<p dir="ltr">Picture a product manager &ndash; call her Sally &ndash; who joins a new team and gets handed Jira and a wiki. She spends weeks figuring out what "Everything" means in the product, because here it isn't a common word, it's a specific screen. An AI tool writing requirements has Sally's first-week problem on every story, except it never asks. It guesses, confidently, and moves on.</p>
<p dir="ltr">Two things drive the drift. The tool doesn't know the product's terminology, so it uses words in their generic sense. And it doesn't know the decisions behind the work, so it writes requirements that contradict choices the team already made. This is the AI context problem in practice &ndash; the tool is generating from a generic understanding of requirements, not your product's actual context.</p>

<p dir="ltr">Only 8% of engineers say a ticket gives them everything they need to start, and ambiguous or missing acceptance criteria is the top cause of delay and rework they report (<a href="https://atono.io/lp/context-gap-report">Context Gap Report</a>). The rest of the friction shows up downstream &ndash; as clarifying questions, rework, and implementations that don't match what was meant.</p>
<p dir="ltr">A note on cause: that missing product context is the primary driver of this drift is a strong argument, but not the only one &ndash; review gaps and thin acceptance criteria contribute too. The fix below helps regardless of how you weight them.</p>

<p dir="ltr">When AI has access to your product's terminology and the decisions behind past work, it stops guessing and starts writing from the same shared understanding your team uses. That terminology lives in a Glossary; those decisions live in <a href="https://atono.io/learning/what-is-ai-context">AI Context</a>; both reach the tool through an MCP server.</p>
<p dir="ltr">This is what missing AI agent context looks like in practice &ndash; supply the reasoning and the output comes back as specs and stories that match your product, with fewer defects and fewer mid-cycle discoveries. Better AI requirements gathering starts with giving the tool something real to work from, not a pattern-matched guess.</p>

<h3>Why does AI write requirements that look right but aren't?</h3>
<p>It generates from patterns of what requirements usually look like, not your product's specific terminology and decisions. The output fits the format while missing the meaning. This is an AI context problem &ndash; the tool is writing from a generic understanding of requirements rather than your product's actual rules and terminology.</p>
<h3>Can't I just review AI-generated requirements more carefully?</h3>
<p>Review helps catch mistakes, but it doesn't fix the underlying issue: the AI is still generating from incomplete product context, so the same classes of errors keep reappearing. Fixing the input is more reliable than catching every bad output.</p>
<h3>Is this an AI problem or a spec problem?</h3>
<p>Both. Human-written specs drift from the product too; AI makes the same mistake faster and at scale.</p>
<h3>What stops AI from generating almost-right specs?</h3>
<p>Giving it your terminology and the decisions behind the work &ndash; structured Product Knowledge served through an MCP server &ndash; so it writes from the actual product instead of a guess.</p>
<h3>How does AI requirements gathering go wrong?</h3>
<p>Most AI requirements gathering fails because the tool has no access to product-specific context &ndash; it doesn't know your terminology, your past decisions, or your business rules. It fills those gaps with plausible language that looks right and isn't.</p>

<p dir="ltr">Product Knowledge is the shared understanding of how a product works, why it works that way, and what its terminology means. For AI tools, Product Knowledge provides the context they need to generate work that matches the product instead of approximating it.</p>
<p dir="ltr">Product Knowledge is to AI what shared understanding is to a team. When two engineers work together, they rely on a common grasp of the terminology, the decisions already made, and the business rules. AI tools don't have that shared understanding by default &ndash; Product Knowledge gives it to them.</p>

<p>AI tools generate work that looks correct but doesn't match the specific product, because they don't share the team's understanding of it. They read your code and your tickets, then fill the gaps with plausible guesses. The result is output that's almost right &ndash; and almost right is the harder problem, because wrong gets caught in review while almost right gets shipped and surfaces later when someone actually uses it.</p>

<p dir="ltr">Four kinds of understanding, all things a team already recognizes:</p>
<ul>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation"><strong>Product terminology</strong> &ndash; what your words mean inside your product, not their generic sense.</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation"><strong>Product decisions</strong> &ndash; what was chosen, and why.</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation"><strong>Product rules </strong>&ndash; the constraints and business logic the product runs on.</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation"><strong>Product history</strong> &ndash; how the product got here, and what's already been tried.</p>
</li>
</ul>

<p dir="ltr">Take a single word. In Atono, "Everything" names a specific screen &ndash; the centralized view of every workspace item across teams, projects, and workflows.</p>
<p dir="ltr"><strong>Without Product Knowledge</strong>. Prompt: "Add Everything to the navigation." The tool reads "everything" as a common word and adds all items to the nav. Wrong.</p>
<p dir="ltr"><strong>With Product Knowledge</strong>. The tool knows "Everything" is a named screen and links to it. Correct.</p>
<p dir="ltr">Every product has dozens of terms like this &ndash; words that look ordinary and mean something exact. Each missed one is a small, confident error, and they compound across every story, spec, and pull request.</p>

<p>Documentation records what someone knew when they last updated the page, and it goes stale the moment the product moves. Product Knowledge is maintained as part of the work and structured for a tool to query, so it reflects what the product means right now. (More in <span style="text-decoration: underline;">Product Knowledge vs Documentation</span>.)</p>

<p>The most common objection from engineers: isn't this already in the codebase? The code tells a tool what the system does. It doesn't explain why a decision was made, which customer problem it solves, what a term means internally, or which tradeoffs were accepted. Product Knowledge captures the meaning behind the implementation &ndash; the part the code can't tell you.</p>

<p dir="ltr">Product Knowledge is the system that closes the context gap; Atono is the platform built on it. Atono holds your Product Knowledge &ndash; a Product Glossary for terminology, AI Context for the decisions behind the work &ndash; and serves it to every tool in your stack through MCP. Atono isn't a Jira replacement; it's the addition that makes your existing stack understand your product.</p>
<p dir="ltr">For evidence that the gap is real and costly, see the <a href="https://atono.io/lp/context-gap-report" target="_blank" rel="noopener">Context Gap Report</a> findings: only 27% of engineers say a ticket is clear on both the problem and what success looks like, and 64% of teams keep critical knowledge primarily in people's heads.</p>

<p>AI tools reach Product Knowledge through an MCP server &ndash; the emerging standard for feeding context into tools like Cursor, Claude Code, and Copilot. Terminology tells a tool what your words mean; <span style="text-decoration: underline;">AI Context</span> tells it the decisions and history behind a specific piece of work. With both in hand, the tool produces output that matches the product on the first pass instead of approximating it.</p>

<h3>What is Product Knowledge in simple terms?</h3>
<p>It's your product's meaning &ndash; how it works, why it works that way, and what its terms mean &ndash; written down in a form both people and AI tools can use. It gives every tool the same understanding of your product, so the work they generate matches it.</p>
<h3>How do you define Product Knowledge?</h3>
<p dir="ltr">Product Knowledge is your product's shared understanding &ndash; its terminology, decisions, rules, and history &ndash; structured so both people and AI tools can query it. Unlike documentation, it's maintained as part of the work, so it stays current instead of going stale between updates.</p>
<h3 dir="ltr">Isn't Product Knowledge already in the code?</h3>
<p dir="ltr">No. The code shows what the system does, not why. It doesn't carry the reasoning behind a decision, the customer problem it solves, what a term means internally, or the tradeoffs that were accepted. Product Knowledge captures that meaning behind the implementation.</p>
<h3 dir="ltr">How is Product Knowledge different from documentation?</h3>
<p dir="ltr">Documentation captures what someone knew when they wrote it and goes stale. Product Knowledge is maintained alongside the work and structured for machines to query, so it stays current.</p>
<h3>What is Product Knowledge management?</h3>
<p dir="ltr">Product Knowledge management is the practice of capturing, maintaining, and distributing your product's shared understanding &ndash; its terminology, decisions, and rules &ndash; in a structured system that every tool and team member can access. Without it, that understanding lives in people's heads and has to be re-explained to every new engineer, stakeholder, and AI tool that joins the work.</p>
<h3 dir="ltr">Why do AI tools need Product Knowledge?</h3>
<p dir="ltr">AI tools read code and tickets but not the intent and terminology behind them, so they guess &ndash; and the guesses produce almost-right output. Product Knowledge replaces the guess with the actual answer.</p>
<h3 dir="ltr">How do AI tools access Product Knowledge?</h3>
<p dir="ltr">Through an MCP server that carries the structured context into tools like Cursor and Claude Code. Terminology comes from the Glossary; decisions and history come from <span style="text-decoration: underline;">AI Context</span>.</p>
<h3 dir="ltr">Does Atono replace Jira or Linear?</h3>
<p dir="ltr">No, by default. Atono adds Product Knowledge to your existing stack and works alongside Jira, Linear, or Azure DevOps. It can replace them when a team wants that, but the default path is the context layer, not a migration.</p>

<p>AI Context is the recorded reasoning behind a piece of work &ndash; the decisions made, the alternatives considered, and the technical changes &ndash; attached directly to the ticket so AI tools understand why something is being built, not just what to build. It's one layer of <a href="https://atono.io/learning/what-is-product-knowledge">Product Knowledge</a>, the shared understanding that lets AI tools match your product instead of approximating it.</p>

<p dir="ltr"><strong>Without AI Context</strong></p>
<ul>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Tickets describe intent briefly, or capture it once and go stale</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Decisions live in Slack, docs, or people's heads</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">AI tools reconstruct the reasoning from guesses</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Every new teammate or agent relearns the same context</p>
</li>
</ul>
<p dir="ltr"><strong>With AI Context</strong></p>
<ul>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Decisions travel with the work</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Tradeoffs and rationale are explicit and structured</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">AI agents inherit intent instead of inferring it</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">New teammates and agents pick up where the last one left off</p>
</li>
</ul>

<p dir="ltr">AI tools are strong at producing output that fits a pattern and weak at knowing your product's specific intent. AI Context supplies the intent. When the decisions and history travel with the work, an agent picks up where the last person left off instead of relitigating settled calls &ndash; which is also why a missing-context failure shows up as <a href="https://atono.io/learning/why-ai-generates-almost-right-requirements">almost-right requirements</a>.</p>
<p dir="ltr">Only 20% of teams consistently maintain Architectural Decision Records, and 64% report that critical knowledge lives primarily in people's heads (<a href="https://atono.io/lp/context-gap-report">Context Gap Report</a>). That gap is exactly what AI Context is designed to close &ndash; by capturing decisions where the work already lives.</p>
<p dir="ltr">This is what ai context engineering looks like in practice: not a separate process, but decisions captured as part of the work so every agent and teammate reads from the same source instead of reconstructing it from scratch.</p>

<p dir="ltr">AI Context typically includes:</p>
<ul>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Design decisions (what was chosen and why)</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Alternatives considered (and why they were rejected)</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Technical investigation (what was explored or tested)</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Implementation changes (what actually shipped)</p>
</li>
<li dir="ltr" aria-level="1">
<p dir="ltr" role="presentation">Links to related work (tickets, docs, PRs)</p>
</li>
</ul>
<p dir="ltr">In Atono, this lives on every story and bug across three tabs &ndash; Design Decisions, Technical Investigation, and Technical Changes. A ticket title and a few acceptance criteria tell an agent what to build; AI Context tells it the reasoning a good engineer would have absorbed from a conversation that never got written down anywhere a tool could read.</p>

<h3>What is AI Context in one sentence?</h3>
<p>The recorded reasoning behind a piece of work &ndash; decisions, alternatives, and changes &ndash; structured and attached to it so AI tools read the why, not just the requirement.</p>
<h3 dir="ltr">What is AI Context in software development?</h3>
<p dir="ltr">AI Context is the structured record of why a piece of work was built the way it was &ndash; the decisions made, the options ruled out, and what changed &ndash; attached to the ticket so AI agents and teammates inherit the reasoning instead of guessing at it.</p>
<h3 dir="ltr">Isn't this just documentation?</h3>
<p dir="ltr">No. Documentation describes the system. AI Context captures the reasoning behind specific changes as they happen, structured and attached to the work itself.</p>
<h3 dir="ltr">How is AI Context different from a ticket description?</h3>
<p dir="ltr">A ticket states what to build; AI Context records why it's built that way &ndash; the decisions made, the options ruled out, and what actually changed.</p>
<h3 dir="ltr">Is AI Context the same as Product Knowledge?</h3>
<p dir="ltr">No. AI Context is one layer of Product Knowledge, which also spans terminology and rules (the Glossary) and how decisions become shipped product.</p>
<h3 dir="ltr">How do AI tools read AI Context?</h3>
<p dir="ltr">Through an MCP server that serves the structured context into the tools you already use, so they apply the actual decisions behind a piece of work. This is how ai agent context reaches tools like Cursor, Claude Code, and Copilot &ndash; through a shared, structured layer any compatible client can pull.</p>
<h3 dir="ltr">What is AI context engineering?</h3>
<p dir="ltr">AI context engineering is the practice of structuring and maintaining the information AI tools need to produce accurate, product-specific output &ndash; rather than letting them fill gaps with plausible guesses. AI Context is one part of that: capturing the decisions and reasoning behind the work so agents can read it directly.</p>

<p>The difference is currency and structure: documentation records what someone knew when they last updated the page, while <a href="https://atono.io/learning/what-is-product-knowledge">Product Knowledge</a> reflects what your product means right now in a form AI tools can read directly. Product Knowledge is the system that closes the context gap; documentation, on its own, tends to widen it.</p>

<p>A wiki or a doc is written once and decays from there. It's prose meant for a human to skim, it goes stale the moment the product moves, and an AI tool reading it can't tell which parts still hold. Product Knowledge is maintained as part of the work itself, structured so a tool can query a specific term or decision, and current because it updates as the product does.</p>
<table style="border-collapse: collapse; width: 100%;" border="1"><colgroup><col style="width: 19.6721%;"><col style="width: 42.9272%;"><col style="width: 37.4006%;"></colgroup>
<tbody>
<tr style="height: 21.9922px;">
<td style="height: 21.9922px;">&nbsp;</td>
<td style="height: 21.9922px;"><strong id="docs-internal-guid-b97ed139-7fff-6876-9ebe-c7d9e207ac24">Documentation</strong></td>
<td style="height: 21.9922px;"><strong id="docs-internal-guid-3817c8fd-7fff-7e85-a669-c89c69a15835">Product Knowledge</strong></td>
</tr>
<tr style="height: 21.9922px;">
<td style="height: 21.9922px;"><strong id="docs-internal-guid-a1ba92db-7fff-f837-bbdc-8c705d0368b6">Reflects</strong></td>
<td style="height: 21.9922px;">What someone knew when they wrote it</td>
<td style="height: 21.9922px;">What the product means right now</td>
</tr>
<tr style="height: 21.9922px;">
<td style="height: 21.9922px;"><strong id="docs-internal-guid-82dc6394-7fff-06de-3b65-6493404bb9fe">Form</strong></td>
<td style="height: 21.9922px;">Prose for humans to skim</td>
<td style="height: 21.9922px;">Structured context tools can query</td>
</tr>
<tr style="height: 21.9922px;">
<td style="height: 21.9922px;"><strong id="docs-internal-guid-b62196b9-7fff-2e75-16a6-3e4bc06e8230">Maintenance</strong></td>
<td style="height: 21.9922px;">Updated when someone remembers</td>
<td style="height: 21.9922px;">Maintained as part of the work</td>
</tr>
<tr style="height: 43.9844px;">
<td style="height: 43.9844px;"><strong id="docs-internal-guid-a5214ea5-7fff-4377-abb6-363b296a34f0">AI can use it</strong></td>
<td style="height: 43.9844px;">Only by guessing what's still true</td>
<td style="height: 43.9844px;">Directly, with terminology and rules intact</td>
</tr>
<tr style="height: 21.9922px;">
<td style="height: 21.9922px;"><strong id="docs-internal-guid-86990c01-7fff-57d3-c6d8-b76ef04c68e9">Failure mode</strong></td>
<td style="height: 21.9922px;">Quietly goes stale</td>
<td style="height: 21.9922px;">Stays current by design</td>
</tr>
</tbody>
</table>

<p dir="ltr">An AI tool fed stale documentation produces stale, almost-right output &ndash; confidently applying a definition or rule that changed two releases ago. Structured, current Product Knowledge removes that guess. The <a href="https://atono.io/lp/context-gap-report">Context Gap Report</a> puts numbers on the problem: 64% of teams keep critical knowledge primarily in people's heads, another 57% have it scattered across tools like Notion, Confluence, and Google Docs, and only 20% maintain Architectural Decision Records. Documentation that thin and that scattered is exactly what leaves a tool guessing.</p>
<p dir="ltr">This is where product knowledge management earns its place &ndash; not as a separate process layered on top of the work, but as the practice of keeping terminology, decisions, and rules structured and current so AI tools can read from the actual product instead of a stale approximation of it.</p>
<p dir="ltr">Documentation still has its place for onboarding and narrative. For making AI tools product-correct, what they need is Product Knowledge.</p>

<h3 dir="ltr">Is Product Knowledge just better documentation?</h3>
<p dir="ltr">No. Documentation is prose written for humans and frozen at the time of writing. Product Knowledge is structured context maintained alongside the work and built for tools to query, so it stays current. Think of it less as a better knowledge base and more as a different kind of artifact entirely.</p>
<h3 dir="ltr">Can I replace my docs with Product Knowledge?</h3>
<p dir="ltr">They serve different jobs. Docs are still useful for narrative and onboarding; Product Knowledge is what makes AI tools understand the product accurately. Most teams keep both.</p>
<h3 dir="ltr">Why does documentation make AI output worse?</h3>
<p dir="ltr">It's maintained as part of doing the work &ndash; terminology in the Glossary, decisions in AI Context &ndash; rather than as a separate document someone has to remember to update.</p>
<h3 dir="ltr">What keeps Product Knowledge current?</h3>
<p dir="ltr">It's maintained as part of doing the work &ndash; terminology in the Glossary, decisions in AI Context &ndash; rather than as a separate document someone has to remember to update.</p>
<h3 dir="ltr">What is product knowledge management?</h3>
<p dir="ltr">Product knowledge management is the practice of capturing, maintaining, and distributing your product's shared understanding &ndash; its terminology, decisions, and rules &ndash; in a structured system that both people and AI tools can access. Unlike documentation management, it's built to stay current as the product evolves rather than requiring someone to remember to update a separate page.</p>

<p dir="ltr">RAG retrieves documents.&nbsp;<a href="https://atono.io/learning/what-is-ai-context">AI Context</a> carries the decisions, constraints, and terminology behind a piece of work. One finds information; the other preserves reasoning. They're both ways of grounding a model, but they solve different halves of the problem &ndash; and the half RAG can't reach is the one that usually causes almost-right output.</p>
<p dir="ltr">The short version: RAG finds the text. AI Context carries the reasoning behind the text.</p>

<p dir="ltr">RAG (retrieval-augmented generation) is a retrieval mechanism. Point it at a corpus and it surfaces the passages most similar to a query. It's powerful for "find the relevant text," and it's only as good as the corpus &ndash; if the docs are stale or the reasoning was never written down, RAG retrieves stale or absent context with full confidence.</p>
<p dir="ltr">AI Context is authored, not retrieved. It's the design decisions, investigation, and changes deliberately attached to a story or bug, structured so a tool reads the actual reasoning rather than a similarity match against prose. It doesn't compete with RAG so much as give it something worth retrieving.</p>
<table style="border-collapse: collapse; width: 100%;" border="1"><colgroup><col style="width: 23.994%;"><col style="width: 38.5944%;"><col style="width: 37.4115%;"></colgroup>
<tbody>
<tr>
<td>&nbsp;</td>
<td><strong id="docs-internal-guid-e9eac885-7fff-7682-e957-e1b15c0f3ac4">RAG</strong></td>
<td><strong id="docs-internal-guid-ad27bc88-7fff-852b-ea75-9f4f77f659a5">AI Context</strong></td>
</tr>
<tr>
<td><strong id="docs-internal-guid-1b391224-7fff-193a-bc05-fabb03b21dad">Mechanism</strong></td>
<td>Retrieves similar text chunks at query time</td>
<td>Authored, structured context attached to the work</td>
</tr>
<tr>
<td><strong id="docs-internal-guid-eb4ad59d-7fff-33f0-91c8-09785b49d641">Source quality</strong></td>
<td>Only as current as the underlying corpus</td>
<td>Maintained as part of the work</td>
</tr>
<tr>
<td><strong id="docs-internal-guid-68dd9ed8-7fff-4879-a85d-5516724fa284">What it captures</strong></td>
<td>Whatever happens to be written down</td>
<td>Decisions, constraints, and the why</td>
</tr>
<tr>
<td><strong id="docs-internal-guid-aeb4e545-7fff-57ab-8e2f-577aa598567e">Answers</strong></td>
<td>"What documents look relevant?"</td>
<td>"Why was this work done this way?"</td>
</tr>
<tr>
<td><strong id="docs-internal-guid-39d83ab4-7fff-6767-3764-822133f762a9">Best at</strong></td>
<td>Finding relevant passages</td>
<td>Applying the actual product reasoning</td>
</tr>
</tbody>
</table>

<p dir="ltr">This is the part teams miss. Imagine a single bug ticket. The acceptance criteria changed after customer interviews. Engineering chose approach B over approach A because of a latency constraint. Product agreed to postpone a related feature until Q4.</p>
<p dir="ltr">None of those decisions exist in formal documentation. They were made in a call, a thread, a hallway. A RAG pipeline can only retrieve what's in its corpus &ndash; so if the reasoning never entered the corpus, retrieval can't recover it, no matter how good the embeddings are. The model is left to guess at intent, and the guess ships as almost-right work.</p>
<p dir="ltr">That's the gap AI Context is built to fill: it captures the reasoning at the moment the work happens, so it's there to be used later &ndash; by a person or a tool. This is AI grounding done at the level of specific decisions, not just documents.</p>

<p dir="ltr">The honest answer is that you usually want both. RAG handles open-ended "where is this written" retrieval across a large corpus. AI Context handles "what was actually decided here," which is the part that's rarely written down anywhere a retriever could find it. Structured <a href="https://atono.io/learning/what-is-product-knowledge">Product Knowledge</a> &ndash; Glossary plus AI Context, served through an MCP server &ndash; is what gives a RAG pipeline current, product-correct material to retrieve from instead of stale prose.</p>
<p dir="ltr">For how often the relevant reasoning is missing entirely: 64% of teams keep critical knowledge primarily in people's heads, and only 20% record architectural decisions (<a href="https://atono.io/lp/context-gap-report">Context Gap Report</a>). You can't retrieve what was never written down.</p>

<h3 dir="ltr">Is AI Context a replacement for RAG?</h3>
<p dir="ltr">No. RAG is a retrieval method; AI Context is structured, authored context. They work together &ndash; AI Context gives a RAG pipeline current, product-correct material to retrieve. Think of it as better AI grounding: RAG finds the text, AI Context makes sure the right reasoning exists to be found.</p>
<h3 dir="ltr">What's the difference in one sentence?</h3>
<p dir="ltr">RAG retrieves information from documents; AI Context stores the decisions and reasoning attached to a piece of work. One finds information, the other preserves reasoning.</p>
<h3 dir="ltr">When should I use RAG vs AI Context?</h3>
<p dir="ltr">Use RAG to find relevant text across a corpus. Use AI Context to carry the specific decisions and constraints behind a piece of work &ndash; which usually aren't written down anywhere a retriever would find them.</p>
<h3 dir="ltr">Why isn't RAG enough on its own?</h3>
<p dir="ltr">RAG is only as good as its corpus. If the decisions and terminology were never captured, or have gone stale, RAG confidently retrieves the wrong or missing context. AI Context captures that reasoning in the first place.</p>
<h3 dir="ltr">How does this relate to Product Knowledge?</h3>
<p dir="ltr">AI Context is one layer of Product Knowledge. Product Knowledge is what you ground tools with &ndash; whether they reach it through MCP, a RAG pipeline, or both.</p>
<h3 dir="ltr">What is AI grounding?</h3>
<p dir="ltr">AI grounding is the practice of connecting a model to specific, accurate information so it produces output based on real facts rather than general patterns. RAG is one grounding mechanism &ndash; it retrieves relevant documents at query time. AI Context is another &ndash; it attaches structured decisions and reasoning directly to the work so the model reads the actual intent, not a similarity match against prose.</p>

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What is Product Knowledge?

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