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Integrate a large language model (LLM) as one component in a controlled application workflow—not as a prompt that gets unrestricted access to customer data or business actions. Keep authorization, business rules, and tool permissions in your application code; add retrieval only when answers need current or organization-specific information; and test the complete feature before and after changes.
How do I integrate an LLM into my SaaS product?
A production LLM feature usually passes through several application components. A useful baseline is:
- SaaS client and backend: Authenticate the user and establish the tenant and access rights in your existing application.
- Application orchestration: Assemble the task, permitted context, and any allowed operations. Apply business rules here, not in model instructions alone.
- Optional retrieval or tools: Retrieve authorized information when the answer needs it, or expose a narrowly scoped operation when the feature must take an action.
- Model API: Send the request and only the context the feature needs.
- Validation and policy checks: Check the response and any proposed action before using or displaying them.
- User-facing response: Return the result with appropriate source context or a clear failure path.
This separation matters because model output is not a trusted command. Your application should remain responsible for identity, tenant boundaries, business policy, and what operations are allowed. The exact components vary by feature, but the control boundaries should be explicit.
Keep permissions outside the prompt
A prompt can explain the task, but it cannot serve as an authorization system. Determine which records and fields a user may access in application services. Enforce those checks before data is sent to the model and again at any data-service or action boundary that needs them. OWASP’s RAG guidance specifically advises dynamically limiting sensitive fields according to the user’s access level.
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Decide what the model is allowed to do
For a feature that only drafts or summarizes, the model may need no tools at all. If it can invoke application functions, expose only the operations needed for the task, validate each call against an allowlist and permitted parameter schema, and check authorization in the function that performs the operation. Require confirmation or additional review where the consequence warrants it. Never execute arbitrary model-generated text as code or treat the model’s own assurance that an action is safe as a control.
Should I use RAG for my SaaS app?
Use retrieval-augmented generation (RAG) when a response needs current information, private organizational material, or customer-specific records that are not reliably available in the model’s built-in knowledge. Retrieval finds relevant material in an external data store and adds it to the model’s context. Google Cloud’s reference architecture describes this retrieve-and-augment pattern, and AWS describes RAG as a way to use external knowledge, including enterprise information that may not be in a model’s fixed knowledge.
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RAG is not automatically the right choice for every LLM feature. If the task can be handled with the user’s request and a small, stable set of instructions, a direct model call may avoid the extra data pipeline. If the feature must change application state, retrieval alone is not enough; it may need a separate, constrained tool workflow.
| Approach | Use it when | Key design concern |
|---|---|---|
| Direct model call | The task does not require retrieving changing or private source material and does not need an application action. | Keep inputs limited to what the task needs, and validate the response for the intended use. |
| RAG | The answer should draw on current, customer-specific, or organization-specific information. | Authorize before retrieval, protect tenant boundaries throughout the data path, and treat retrieved text as untrusted input. |
| Tool-connected workflow | The feature needs to query a system or perform an application operation. | Constrain tools and parameters, verify authorization in application code, and control consequential actions. |
Choose among these patterns by asking whether the answer needs current or private data, how tenant authorization will be enforced, whether an action is reversible, whether users need to trace an answer to its sources, and how added latency and operating cost fit the product. These are design trade-offs, not a ranking of model providers. A workflow can combine patterns, such as retrieving authorized records and then using a limited tool, but each added component creates another boundary to secure and evaluate.
What RAG adds to the system
RAG is a pipeline, not simply a vector search call. It adds work for ingesting and updating data, creating embeddings, storing content, retrieving relevant passages, applying authorization, and handling the generated answer. Google Cloud’s reference architecture describes retrieval from a managed datastore before generation. Each stage needs an owner and an explicit access policy, especially when records from multiple tenants share infrastructure.
How do I stop prompt injection in RAG?
You cannot make retrieved content trustworthy merely by telling the model to ignore instructions in documents. A document can contain adversarial text that tries to redirect the model or induce a data disclosure or action. OWASP’s RAG Security Cheat Sheet warns that risk is redistributed across the data pipeline, from ingestion through generation, rather than removed by adding retrieval.
- Authorize retrieval in application code. Scope the search to the authenticated user’s tenant and permitted records before results enter the model context. Do not ask the model to decide what the user is allowed to see.
- Limit sensitive fields. Return only the fields needed for the task, applying the user’s access level at the service boundary.
- Treat documents and user input as untrusted. Ingestion scanning and filtering can help, but neither makes prompt wording a sufficient security boundary.
- Constrain tools independently. A malicious passage should not be able to expand the model’s capabilities. Use an allowlist, schema validation, and application-side authorization for every operation.
- Validate output and actions. Check that responses and proposed operations meet application policy before disclosure or execution.
- Test adversarial cases. Include hostile documents, malformed inputs, and attempts to cross tenant boundaries in regular robustness and red-team exercises.
OWASP’s guidance covers risks from ingestion to downstream agents. That breadth is important: a filter at one stage cannot substitute for access control and validation at the stages that actually expose data or perform actions.
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How do I evaluate LLM features in production?
Evaluate the complete application chain, not just the model in isolation. A useful evaluation set reflects real user tasks, expected outcomes, and known failure cases. It should cover the parts of the workflow that can change the result: retrieval, prompt construction, model behavior, validation, and any downstream API or tool.
Best Value
Build checks around product outcomes
- Task success: Does the workflow complete the user’s intended task?
- Grounding: When the answer relies on retrieved material, is it supported by the material actually provided?
- Safety and authorization: Does the system refuse or safely handle unauthorized requests, hostile content, and disallowed actions?
- Latency and cost: Are response time and operating cost acceptable for this feature and its usage pattern?
These are practical dimensions to select according to the product; Google guidance supports programmatic evaluation and tracking versions but does not prescribe one universal metric set. Use the same relevant evaluation cases to compare changes to prompts, retrieval, models, and other components, and investigate regressions before rollout.
Make changes traceable
Record the prompt, model, and component versions used for evaluation results. Otherwise, a changed response may be difficult to attribute to a new prompt, a different model, altered retrieval behavior, or a downstream dependency. Keep evaluation part of the change process and continue monitoring after release; a passing offline set is useful evidence, not proof that every production interaction will be safe or correct.
What should secure development include?
Threat-model the feature around its actual data, users, integrations, and possible actions. Include cross-tenant disclosure, prompt injection, malformed input, tool misuse, and failure in downstream services where they apply. Sanitize and validate external and user input in application code, and repeat adversarial testing as prompts, data sources, models, or tools change.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsNIST SP 800-218A, published July 26, 2024, augments the Secure Software Development Framework with practices for generative AI and dual-use foundation models. NIST identifies model producers, AI-system producers, and acquirers among its intended audiences. It is a secure-development reference to use alongside threat modeling for the particular SaaS application, not a substitute for application-specific access controls or operational testing.
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