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Prepare knowledge content for an AI support agent by turning reliable support guidance into focused, clearly structured, permission-aware articles—and then testing whether the system retrieves and uses those articles correctly. Start with recurring customer needs, give each article one job, document prerequisites and exceptions, label versions and audiences, and keep the content current. Retrieval can ground an answer in your material, but it cannot make stale or incorrect material true.
1. Start with the support questions customers actually ask
Build the knowledge base around recurring customer scenarios, questions, and problems—not around the organization chart or every ticket that has ever been filed. Salesforce recommends choosing article topics from typical scenarios and problems in its source-content guidance.
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Look for issues that recur or have a reusable resolution. A one-off ticket may be better handled as a case than promoted into permanent guidance. Before creating an article, identify the customer intent it serves: for example, changing a billing detail, troubleshooting a login, or returning an order. If a procedure has materially different paths for different products, regions, or account types, make those distinctions explicit rather than implying one answer applies to everyone.
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An article should answer a recognizable support need. Avoid combining loosely related questions into one long page: when retrieval selects only a fragment, the answer may lose context or bring in a nearby but irrelevant procedure. Salesforce specifically cautions that fragment-based retrieval can reduce coherence when loosely related subtopics are combined.
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- Use the words customers use for the issue, alongside the product’s official terminology.
- Use product and feature names consistently. Define abbreviations the first time they matter, and identify deprecated names if customers may still search for them.
- Separate distinct tasks, such as setup and troubleshooting, when each can be answered independently.
- When two paths differ by plan, platform, region, or user role, name the applicable condition in the relevant section.
3. Structure the content so a relevant passage stands on its own
Use meaningful headings and short, coherent sections. A reader—and a retrieval system selecting only part of an article—should be able to tell what the passage addresses and when it applies. AWS recommends semantically rich, well-structured, self-contained source units in its RAG writing guidance; Salesforce likewise recommends heading hierarchy and meaningful fields.
Where the knowledge platform provides separate fields, use them instead of burying everything in a single text block. Useful fields may include:
- Question or issue: the customer need the article addresses.
- Description and applicability: the product, feature, account type, region, or version covered.
- Prerequisites: permissions, tools, settings, or information needed before starting.
- Resolution: ordered, actionable steps with the expected result.
- Exceptions and recovery: known variations, failure symptoms, and what to do next.
- Escalation: when self-service should stop and how to reach the appropriate support path.
There is no universal article length or chunk size established by the guidance cited here. Keep sections as small as clarity allows, but preserve the conditions and context needed to use each instruction safely. Adjust boundaries based on retrieval tests rather than adopting an arbitrary word-count rule.
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4. Include enough context to make instructions safe
Concise does not mean incomplete. A step that works only for a particular software version, environment, or permission level can mislead if that condition is omitted. For procedures, state the relevant prerequisites and assumptions, identify the applicable product or version, describe what success looks like, and explain exceptions or recovery steps.
- Write steps in the order a customer should perform them.
- Name settings, screens, or options precisely when those labels are known and stable.
- Explain what the customer should see after an important step, so they can detect a problem before continuing.
- Include common mistakes or examples when they distinguish similar cases.
- For screenshots or diagrams, provide descriptive captions or alt text. If essential instructions exist only inside an image, the ingestion system needs to process that visual information; text around the image is not a substitute.
Salesforce’s source-content recommendations emphasize usable context and structured guidance. The goal is not to add boilerplate to every page, but to include the information needed to apply the answer correctly.
5. Separate audiences and enforce access in retrieval
Customer-facing instructions, internal troubleshooting notes, and developer procedures often differ in both detail and sensitivity. Separate them into appropriate articles or fields, and label the intended audience and access tier. Do not assume that a sentence such as “internal use only” will keep confidential material out of a customer-facing answer.
Apply permissions in the retrieval system so it filters sources according to the current user and use case. AWS discusses grounding and access controls in its grounding and RAG guidance. Access control should be enforced by the system, not left to the agent’s interpretation of article text.
6. Add metadata that helps identify the right article and version
Metadata can help filter content and distinguish similar procedures. Choose fields that map to actual retrieval, maintenance, or permission needs; inconsistent or unnecessary fields create noise rather than clarity.
| Candidate field | What it helps distinguish |
|---|---|
| Product or feature | Which product area the guidance applies to. |
| Audience and access tier | Whether the content is intended for customers, agents, developers, or another authorized group. |
| Language and region | Which localization, policy, or regional variation applies. |
| Version | Which product or procedure version the article covers. |
| Publication or review date | When the article was published or most recently checked. |
| Content owner | Who is responsible for resolving questions and maintaining the article. |
AWS recommends source classification and traceability in its grounding guidance. Use metadata to support concrete decisions—for example, filtering internal sources from customer sessions or preferring guidance for the active product version—not simply because a field is available.
7. Review for accuracy, conflicts, and freshness
Grounding an answer in a source does not guarantee the source is correct. Salesforce warns that incorrect knowledge may be repeated confidently, while AWS calls for freshness policies and update management. Have a subject-matter expert or authoritative product guidance confirm important instructions. Resolve contradictions and duplicates before indexing; mark superseded procedures so old and new versions are not presented as equally applicable.
Connect maintenance to the changes that can make an article wrong: product releases, policy revisions, regional changes, and regulatory updates. When source content changes, refresh or reindex it as required by the retrieval system. Keep applicability conditions and effective dates visible in the content when they affect the answer.
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Use representative support questions to test two different things: whether the system finds the right material, and whether the model answers faithfully from what it found. OpenAI’s accuracy guidance distinguishes retrieval errors from model errors and recommends evaluation before tuning.
- Create representative questions. Include common issues, wording variations, and cases where similar products or policies require different answers.
- Record the expected source. For each question, identify the article or articles that should support the answer, plus criteria for an acceptable response.
- Inspect retrieval first. Check whether the system returns the right source and whether irrelevant material crowds it out. If the correct article is missing or buried, improve the content, metadata, permissions, indexing, or retrieval configuration as appropriate.
- Inspect the generated answer second. Given the retrieved material, check whether the response follows its conditions, preserves exceptions, and avoids unsupported claims. If retrieval is right but the answer misuses the evidence, address generation behavior rather than rewriting a sound article blindly.
- Repeat after changes. Re-run questions affected by content, indexing, permission, or model changes to catch regressions.
Google Cloud Agent Assist recommends a golden set of about 20–30 examples and two to five relevant articles per example in its evaluation guidance. Those figures are recommendations for that product, not a universal minimum, performance guarantee, or independently validated threshold for every support system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Use real conversations to improve the system
After launch, review weak or failed answers, missing topics, wrong-version retrieval, irrelevant results, and user feedback. Diagnose the failure before changing anything:
- The needed answer does not exist or is incomplete: add or correct source content.
- The right content exists but is not selected: examine article structure, metadata, access filters, indexing, and retrieval settings.
- The retrieved evidence is relevant but the answer distorts it: examine generation behavior and how the agent is instructed to use evidence.
- The content is stale or contradicted elsewhere: update it, resolve duplicates, and refresh the retrieval source.
Salesforce, AWS, and OpenAI each describe parts of this improvement loop in their respective content guidance, grounding guidance, and accuracy guidance. Prioritize fixes based on observed failures rather than assuming every poor answer is a prompt problem.
How retrieval fits into an AI support flow
In a retrieval-augmented system, a customer’s question can be used to find relevant knowledge resources, which are then provided to a solution generator along with the question. Google Cloud describes this pattern in its customer-support architecture, last reviewed December 16, 2025. It is an example architecture, not a requirement to use a particular cloud, model, or storage system.
Retrieval can give a model access to curated domain knowledge without retraining it, but it does not guarantee a truthful answer. Source accuracy, retrieval relevance, permissions, freshness, and the model’s use of evidence all need controls and evaluation, as AWS and OpenAI explain in their grounding guidance and accuracy guidance.
Frequently Asked Questions
How should I structure support articles for an AI agent?
Make each article answer one recognizable support intent. Use meaningful headings and coherent sections, and separate fields such as question, applicability, prerequisites, resolution, exceptions, and escalation when your platform supports them.
What metadata should I add to knowledge articles?
Consider product, feature, audience, language, version, publication or review date, region, access tier, and content owner. Keep fields that support real filtering, permissions, version identification, or maintenance needs.
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How do I know whether the AI is finding the right answer?
Use representative questions with an expected source and acceptable answer criteria. First check whether retrieval selected relevant material; then check whether the model used that material faithfully.
Is there a recommended article length or chunk size?
The cited guidance does not establish a universal length or chunk-size rule. Use clear, self-contained sections and tune article boundaries against retrieval results.
Does retrieval-augmented generation guarantee accurate support answers?
No. It can provide curated knowledge to a model, but cannot correct inaccurate or stale sources or guarantee that retrieval and answer generation will behave correctly.
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