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No. An LLM can interpret a request and propose what should happen; your application must verify that the request is allowed, perform the operation, and record the result. A model saying “Your pizza has been ordered” does not establish that a restaurant received the order, payment succeeded, or the order was saved. That distinction is the practical lesson behind the indexed excerpt for Agrima Gupta’s article; its search result showed “Posted on Sep 16” without a year.
What an LLM can do—and what it cannot prove
A language model can interpret natural language, extract details, summarize information, draft a response, and select a capability your application makes available. Its output is a suggestion or a request for action. It is not proof that an action occurred.
Consider “Order a large pepperoni pizza to my home.” A model might identify the item, size, address, and intent. But the application still has to establish which address is on file, whether the restaurant is open and has the item, whether the user may place the order, and whether payment can be authorized. It must submit the order, persist the result, and report the actual outcome—not the outcome the model predicted.
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The boundary is straightforward: the model interprets and proposes; the application authorizes, validates, executes, and records.
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What belongs to the application
Keep the authoritative parts of your system under application control. That includes authentication and authorization, business rules, validation, transactions, payments, API calls, security controls, and persistent data. The model can help prepare an action, but application code should decide whether to perform it and verify the result.
- Permissions: Check the user’s identity and whether that user can perform the requested operation.
- Rules and input: Validate values against the rules your application actually enforces; do not treat plausible-looking model output as trusted input.
- Side effects: Make payments, update records, or call external services through controlled application logic.
- State and confirmation: Save the outcome and base the response to the user on that confirmed state.
Choose an integration pattern around the work
There is no requirement to put every model call in a separate service or build an agent framework. An existing backend can host the integration. Choose the call pattern based on whether someone is waiting, how failure should behave, whether current application data is needed, and whether the model is adding a feature or replacing existing logic. The six patterns below are described in Learn Backend’s guide, last verified August 23, 2026: How to Add an LLM to Your Existing Backend Without Rewriting It.
Interactive request: call inline when the user needs an answer now
For a feature that needs a model response in the current request, call it from the endpoint and set a hard timeout that fits the endpoint’s latency budget. Decide in advance what to return if the model times out or is unavailable. If the model feature is optional, a degraded response can let the rest of the application continue.
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Background work: use a queue worker when nobody needs to wait
For batch processing or enrichment that does not need to block a user’s request, put work on a queue and let a worker call the model. Make retries, backoff, dead-letter handling, concurrency limits, and idempotent writes explicit. Idempotency matters because retries can repeat work; the application should prevent a repeated job from creating duplicate side effects.
Shared infrastructure: add a gateway when multiple callers need it
A shared internal gateway can centralize provider keys, model routing, caching, and spend attribution across features or teams. It can also become needless ceremony if there is only one call site. Start with the coordination problem you actually have rather than creating a service simply because the integration uses an LLM.
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Grounded answers: retrieve application data only when needed
If a model needs private or current application data that is not already in its context, retrieve relevant data and supply it as context. This is retrieval-augmented generation (RAG). The Learn Backend guide shows PostgreSQL with pgvector as one option for storing and retrieving embeddings, but says a small corpus that fits in the prompt may not need a retrieval system.
Retrieval supplies information; it does not grant permission to act on that information or make the model’s output authoritative. The application remains responsible for validating and enforcing the resulting operation.
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Event-driven enrichment: let a consumer handle non-blocking updates
If an existing domain event can trigger enrichment and the original workflow does not need to wait, attach an asynchronous consumer. For example, a consumer can generate a label after a record is created. Parse and validate the generated value before saving it, and keep the original workflow independent of the model’s availability when appropriate.
Replacing existing logic: compare in parallel before cutover
When considering a model in place of deterministic logic, keep serving the incumbent behavior while running the model on a flagged parallel path. Compare results, inspect disagreements, and cut over gradually only when you understand the differences. This avoids making a new model’s unverified output the live decision by default.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reliability controls that apply across patterns
- Set explicit timeouts and define the degraded behavior before an outage or slow response occurs.
- Validate model output against the expected schema or allowed enum before writing it or using it to trigger an operation.
- Keep model calls outside database transactions. A slow or failed provider call should not hold a transaction open.
- Set output-token limits and meter usage so the application can control cost.
- Handle rate limits with backoff, and make queued work safe to retry.
- For consequential actions, let application logic enforce permissions and rules, execute the action, and confirm its persisted result.
A quick decision guide
| Need | Pattern | Key design decision |
|---|---|---|
| The user needs the model-assisted result in the current interaction | Inline endpoint | Set a timeout from the endpoint budget and define the degraded response. |
| The work can finish after the request returns | Queue worker or event consumer | Define retries, backoff, dead-letter behavior, concurrency, and idempotent writes. |
| Several features or teams need shared model controls | Internal gateway | Centralize shared concerns only when there is a real coordination need. |
| The answer needs current or private application data | Retrieval | Retrieve relevant data; avoid extra infrastructure if a small corpus fits in context. |
| A model may replace existing deterministic behavior | Feature-flagged parallel path | Keep incumbent logic live, compare results, and examine disagreements before cutover. |
For any of these, ask who is waiting, what happens on timeout or provider failure, what data the model needs, and which application controls must run before output can be saved or acted on.
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