An AI-enriched feed keeps the original record and adds something useful beside it: a suggested category, a concise summary, an extracted organization name, or a routing hint. The important word is beside. A generated interpretation should not silently replace the source or acquire the authority of the original publisher. Readers need to see what was supplied, what was inferred, and what still requires review.
A good first project is deliberately narrow. For example, suggest one category for each item in a small editorial queue. That task is easier to evaluate than a general promise to understand every incoming article. This guide develops an enrichment workflow around explicit outputs, evidence, and recoverable failure. The AI Feeds API overview provides the broader topic map; here, the focus is the boundary between source content and an assisted interpretation.
Begin with the decision, not the model
Write down what changes when the enrichment succeeds. Does an editor find relevant items faster? Does a reader receive a shorter preview? Does a support team see a proposed routing label? If nobody can describe the downstream decision, adding a model call may simply produce more fields to maintain.
Choose one output with a clear acceptance rule. A category suggestion might be one member of an approved vocabulary plus a short supporting excerpt. A summary might have a length range and a rule against adding details absent from the source. Avoid asking for several unrelated tasks in the first version. A small, interpretable result makes it easier to compare assisted behavior with a deterministic baseline or a human-written reference.
Preserve a source layer and a derived layer
Keep the original title, content, link, source identity, and observation information separate from the enrichment. Store the enrichment’s task version, model identifier as available, creation time, output, and validation state. That structure lets you revise a prompt without pretending the publisher changed its article.
Consider an item whose source title says “Preview announced.” If an enrichment labels it “General release,” a reviewer should be able to compare that label with the original wording immediately. A combined record that retains only the new label makes the mistake harder to discover. A separate derived layer also allows you to invalidate outdated interpretations when the source is corrected, while keeping the source history intact.
Treat retrieved text as data, not authority
The OWASP LLM Prompt Injection Prevention Cheat Sheet describes how untrusted content can contain instructions that try to redirect a model. It discusses layered defenses, separation of instructions from data, limited privileges, validation, and human oversight. No single text filter should be treated as a complete solution. For a feed workflow, the relevant boundary is between the application’s task instructions and the externally supplied material being analyzed.
A small enrichment worker can be designed without permissions to send messages, modify records outside its output area, or retrieve arbitrary private data. Even if a malicious article tries to redirect the task, the worker should not have unrelated capabilities to exercise. Keep task configuration outside the source payload. Explicit boundaries do not guarantee perfect model behavior, but they limit the consequences your application permits.
Define the output before writing the prompt
For a categorization task, specify the allowed labels and the meaning of an unknown result. Include a short evidence field when it helps a reviewer, but do not accept arbitrary generated URLs as proof. Require the evidence to correspond to the supplied record or to identifiers the application already recognizes.
For a summary, define the intended reader and the maximum scope. “Summarize this article” leaves many choices unresolved. A more concrete task might request the main change, the affected audience, and any stated limitation, with an explicit instruction to leave missing information absent. Validate the result structurally before displaying it. Structural success still does not establish factual correctness, so keep that check separate from editorial acceptance.
Build a test set with real disagreements
A useful evaluation set includes more than easy examples. Add a short announcement, a long discussion, a correction, an opinion piece, and a document whose headline overstates its body. Include an item that does not fit any approved category. These cases reveal whether the workflow can decline to force an answer when the source does not support one.
Have reviewers define what a successful output would accomplish, rather than insisting on one exact wording for every summary. Track meaningful error categories: invented detail, missing qualification, wrong category, unsupported certainty, or failure to preserve attribution. Keep the source records and task versions attached to the evaluation. Without that context, a better-looking score may reflect a changed test set rather than an improved workflow.
Make abstention a normal result
A worker should be able to return “insufficient evidence,” “unsupported language,” or “needs review” when those states are appropriate to the task. Treat these as designed outcomes, not exceptions to hide. A readable queue of unresolved items can be more valuable than a fully populated collection of unreliable labels.
Decide what the interface does with each state. A missing summary can fall back to the source excerpt. An uncertain category can remain unassigned. A parsing failure can show the original title and link without any derived fields. Avoid replacing an unavailable enrichment with an empty string that looks like successful processing. The distinction between not attempted, failed, and reviewed should remain visible to operators.
Keep the workflow bounded
Set limits on input size, processing attempts, and the number of items handled in a batch. Decide whether long content is rejected, sampled, or divided before analysis. Each option changes the evidence available to the model, so include that choice in the task record. Truncation should not be invisible when it removes material a summary might need.
Use a fixed task rather than an open-ended agent when a fixed task is enough. A classifier does not need to plan arbitrary tool calls. A summary worker does not need permission to publish its own output. Separating suggestion from publication keeps the first version easier to understand and test. Only add additional capabilities when a concrete use case justifies their operational and review costs.
Handle source corrections deliberately
When an item changes, compare the accepted source version with the version used for enrichment. Decide whether to rerun the task immediately, mark the output stale, or send the item for review. A small punctuation change may not matter; a corrected date or withdrawn claim may matter a great deal. The rule should match the editorial risk of the field you produce.
Keep earlier outputs when your review process needs to explain what readers previously saw. Do not silently associate an old summary with a new source body. A simple source fingerprint can help detect the mismatch, while stable item identity keeps the correction connected to the same logical record. The news feed workflow guide explores why revisions and attribution deserve their own treatment.
Evaluate cost at the workflow level
Measure the work required to produce an accepted result, not only the work required for a model response. Your own pilot should include repeated attempts, rejected outputs, manual review, and reruns after source corrections. These costs vary with the task and the content, so avoid assuming that a shorter prompt automatically makes the whole process cheaper.
Compare enrichment against a simpler approach. A small keyword rule or a source-provided category may already satisfy a routing need. An AI step earns its place when it improves the intended outcome enough to justify the additional checks. Keep the baseline in your evaluation so later changes do not turn a modestly useful workflow into a more complex system with no visible benefit.
Ship an interpretation people can inspect
A useful AI feeds workflow gives the reader more context without obscuring the evidence. Start with one task, retain the original record, constrain the output, and design review states before expanding the source list. Keep permissions narrow and record the dependencies behind each result. The strongest first release is not the one that fills every field automatically. It is the one that makes a helpful suggestion and shows enough of its basis for someone to judge it.
