You follow a RAG quickstart step-by-step, paste the code, and then hit a wall. Your deployment target is the stable API, but the example uses a preview version with capabilities your general availability (GA) endpoint does not expose. This friction is a defining challenge for modern AI onboarding docs.
The Azure AI Search knowledge base reference makes this structural gap visible. It distinguishes between two API versions: the 2026-04-01 GA version, which supports minimal extractive retrieval, and the 2026-05-01-preview version, which enables query planning, answer synthesis, and configurable reasoning effort. While the page uses “Important” callouts and side-by-side code blocks to signal this split, it can feel like an afterthought rather than a core design principle.
A well-designed RAG quickstart must treat version availability as a first-class constraint from the very first line. By framing the guide around the actual boundaries of the API, you help readers avoid silent capability gaps before they write a single line of code.
The version split your AI quickstart guide must surface
Most RAG quickstarts present a single code path, assuming the reader targets the latest API version. This approach fails when production environments split between GA and preview releases. In the Azure AI Search knowledge base documentation, the 2026-04-01 GA version supports only minimal extractive retrieval. Conversely, the 2026-05-01-preview version unlocks query planning, answer synthesis, and configurable reasoning effort. Ignoring this distinction creates a structural gap in any AI quickstart guide.
Hiding this split in a footnote or a “see also” link is insufficient. A developer who writes code against the preview version but deploys to GA will encounter silent capability gaps. The “Important” callout at the top of the reference page serves as the minimum acceptable warning. However, a well-designed quickstart should lead with this information. Doing so teaches the reader to think in versions from the first line of code.
A robust retrieval guide structure treats version availability as a first-class design constraint, not an afterthought. Before writing any code, the reader must know which features are locked to which API version. AI onboarding docs that prioritize clarity over brevity ensure that developers make informed decisions about their deployment target, avoiding the friction of reverse-engineering capability limits after the fact.
Minimal vs. full configuration: the two-path onboarding pattern
The reference page presents two distinct code examples side-by-side, and the distinction is more structural than superficial. The minimal configuration includes only the knowledge sources and basic metadata like name and description. The full configuration adds Azure OpenAI model parameters, retrieval instructions, answer instructions, output mode, and reasoning effort. This is not a “starter versus advanced” split for different skill levels. It is a direct map of the capability boundary between the 2026-04-01 GA version and the 2026-05-01-preview version.
This pattern offers a reusable rule for any AI onboarding docs: every RAG quickstart should present at least two configuration tiers, each explicitly labeled with the API version it targets. The minimal path provides a working baseline for production-ready GA environments. The full path unlocks the specific feature set, such as query planning and answer synthesis, that requires the newer preview version. The reader chooses their path based on their deployment target, not their patience.
The parallel, not sequential, structure
A critical structural implication is that these two paths must not be sequential. They are parallel options. A developer using the GA version should never be forced to read through preview-only parameter blocks to reach a working example. If the documentation follows a “step 1 then step 2” flow, it implicitly suggests that the GA path is a preliminary stage to be abandoned. This is misleading for teams with stable production constraints.
The reference page’s tab-based navigation, separating the 2026-05-01-preview and 2026-04-01 versions, is the correct user experience pattern for this scenario. It allows the reader to isolate the relevant code block immediately. By treating the version choice as the primary filter, the documentation respects the reader’s context. The retrieval guide structure should reflect this by keeping the code examples distinct, ensuring that the minimal configuration remains a complete, standalone valid snippet for the GA version, while the full configuration remains the complete, standalone valid snippet for the preview version.
Progressive disclosure in retrieval guide structure
The reference page separates the core operational flow from advanced configuration. The create, query, and delete lifecycle appears first, establishing the basic retrieval path. Only after this foundation is laid does the document introduce the CORS configuration section, which is specific to the 2026-05-01-preview version. This ordering prevents the main task flow from becoming cluttered with optional parameters. The “at your own risk” warning inside the CORS section serves as a model for inline risk disclosure, alerting the reader to potential security implications without interrupting the primary narrative.
A RAG quickstart should mirror this hierarchy in its navigation. The table of contents must reflect the task lifecycle, with version-specific features nested as indented children under their relevant tasks. This approach makes the AI retrieval quickstart scannable for both new and experienced developers. A reader who only needs basic extractive retrieval follows a clean, linear path. A developer who requires answer synthesis or custom reasoning effort locates the additional configuration immediately, without re-reading the base example. This structure ensures that the retrieval guide structure supports both simple and complex use cases simultaneously.
By organizing content this way, the document respects the reader’s time and specific needs. It avoids forcing a linear reading of irrelevant advanced features while still providing access to the full capability set. The result is a flexible onboarding document that scales with the complexity of the API.
Building a reusable framework for any RAG platform
The Azure AI Search example provides a strong model for structuring complex AI onboarding docs. We can distill its specific patterns into a four-part structural checklist applicable to any retrieval guide structure:
- Place a version-availability callout at the top that explicitly lists what is and is not supported per API version.
- Present parallel minimal and full code examples, with each block clearly labeled by its target version.
- Organize the content by task lifecycle—create, query, configure advanced, delete—while nesting version-specific features as indented sub-sections under the relevant task.
- Ensure every code block and parameter description carries an explicit preview or GA tag.
Handling multiple languages adds a layer of navigation complexity. The reference page displays C#, Python, and raw HTTP for every operation. In a retrieval guide structure, this requires a specific tab hierarchy. The version split (GA vs. preview) must be the primary tab, while language selection serves as the secondary split. This forces the reader to choose their API version before selecting their preferred syntax. Reversing this order confuses users who may read a C# example for the preview version while deploying to GA, leading to immediate compatibility errors.
This framework is a tool, not a rigid template. It delivers the most value when a platform maintains distinct API versions with meaningful capability gaps, such as the difference between minimal extractive retrieval and advanced answer synthesis. If you are documenting a single-version RAG API, the structure collapses. In that scenario, the version callout becomes redundant, and the document reverts to a standard minimal-to-full progression. The key is recognizing when to apply the full framework and when to simplify the retrieval guide structure to match the actual complexity of the API.
Common questions about structuring AI onboarding docs
Should a RAG quickstart include all API versions or just the latest?
Include every version still in active use. The reference page keeps 2026-04-01 (GA) alongside 2026-05-01-preview because both are in production. Dropping an older version strands developers mid-migration, forcing them to reverse-engineer the capability gap on their own.
How much detail belongs in the “Important” callout at the top?
List the specific feature names that differ, such as query planning, answer synthesis, and configurable reasoning effort. Avoid vague phrasing like “some features are limited.” The reader needs to know exactly what they lose by staying on the GA version so they can make an informed deployment decision before writing any code.
Does this retrieval guide structure work for platforms covering multiple services?
Yes, but scope the version-availability callout per service. If a RAG platform has separate indexing, retrieval, and answering services, each gets its own version callout at the section level. The minimal/full split then applies within each service’s examples, keeping the AI onboarding docs organized and uncluttered.
As preview-to-GA cycles accelerate, version complexity in RAG quickstart docs is becoming the default rather than the exception. The developers who feel lost in a quickstart are often not missing skill — they are missing a document that treats their version constraint as a design input from line one. When the next preview feature you need lands, will your onboarding doc have already made the path clear, or will you be writing the callout yourself in a sidecar README?
