Integrate a large language model into a SaaS product as one component in an application workflow—not as a prompt that gets to make product decisions. Your backend should control what information the model receives, which tools it can request, what permissions apply, and whether its output is safe and useful enough to show or act on.
How do I integrate an LLM into my SaaS product?
A practical flow is: SaaS client and backend → application orchestration → optional retrieval or tools → model API → output validation and policy checks → user-facing response. The orchestration layer gathers permitted context and calls the model; application code remains responsible for authorization, business rules, and any actions that change data or affect users.
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Keep the model’s role explicit. It can interpret a request, draft an answer, or propose a tool call. Its text is not a trusted command, an authorization decision, or proof that an operation succeeded. The application should validate the response and independently confirm any action through the relevant service.
- Choose a bounded user task. Define what the feature should do, what a successful result looks like, and which cases it should decline or hand back to a person.
- Build the request in your backend. Authenticate the user, apply tenant and resource permissions, and include only the context needed for that task.
- Call the model through an orchestration layer. Keep prompts, model selection, retrieval, and tool handling in a path you can change and evaluate, rather than making the client responsible for trusted decisions.
- Validate before returning or acting. Check output shape and applicable product rules. If the model proposes an operation, validate the tool name and parameters against an allowlist and schema before the application considers executing it.
- Measure the end-to-end feature. Test the complete chain—including retrieval and downstream services where used—against realistic tasks and known failure cases.
Google’s programmatic-evaluation guidance treats prompts, models, and other components as parts of a changing system, rather than assuming the model alone determines product quality. That distinction matters when a prompt edit or retrieval change can alter the result even if the model itself has not changed.
#1 Best Overall
Should I use RAG for my SaaS app?
Use retrieval-augmented generation (RAG) when the answer depends on information that is customer- or organization-specific, changes over time, or is not reliably available in a model’s fixed knowledge. Retrieval finds relevant material in an external data store and adds it to the context used for generation. Google Cloud’s reference architecture and AWS’s RAG guidance describe this pattern for grounding generation in external information.
RAG is not automatically the right choice for every LLM feature. If a task does not need private or changing information, retrieval may add data handling, latency, and operational complexity without solving a real product need. If answers require private data, retrieval must respect the same access rules as the rest of your SaaS product.
Rank #2
| Approach | Useful when | Main design concern |
|---|---|---|
| Direct model call | The task can be completed from the user’s request and context that the application can safely provide. | Keep the input bounded; validate the output and do not treat it as a trusted decision. |
| RAG | The answer needs current or organization-specific source material. | Authorize access before retrieval, treat retrieved text as untrusted, and assess whether the answer is grounded in the right material. |
| Tool-connected workflow | The feature needs information or operations from application services, such as looking up a record or proposing a change. | Use narrow, allowlisted tools; validate parameters and enforce permissions in application code before any operation. |
These are architecture choices, not a provider ranking. Decide by asking whether answers need current or private data, how tenant isolation will be enforced, whether actions are risky or reversible, whether users need source traceability, and what latency, operating cost, and evaluation burden the workflow can support. A system may combine retrieval and tools, but each additional component creates behavior and security boundaries to manage.
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How do I stop prompt injection in RAG?
You cannot make retrieved text trustworthy by asking the model to ignore instructions inside it. A document may contain adversarial instructions that influence model behavior. OWASP’s RAG Security Cheat Sheet cautions that the pattern redistributes risk across the data pipeline, from ingestion through generation and downstream agents.
- Authorize before retrieval. Apply tenant, user, and resource access checks in application services and the data layer. Do not ask the model to decide which customer’s information the user may see. OWASP also recommends dynamically limiting sensitive fields according to the user’s access level.
- Handle retrieved content as untrusted input. Ingestion checks, scanning, and filtering may help, but they cannot replace constrained permissions and validation. A retrieved passage can contain instructions as well as facts.
- Limit available tools. Expose only the operations needed for the task. Check a proposed tool name and each parameter against an allowlist and permitted schema; do not execute arbitrary model text.
- Enforce product policy outside the model. The application—not the prompt—must make authorization and business-rule decisions. Validate generated results and independently verify action outcomes.
- Test adversarial cases regularly. Include hostile or malformed user input and retrieved content in robustness and red-team testing. OWASP’s guidance covers risks across the RAG pipeline, not just the prompt sent at generation time.
These controls reduce reliance on the model following instructions, but they do not establish that every attack will be detected. Design the workflow so that a failure to follow the prompt cannot grant access or trigger an unauthorized operation.
How should I evaluate an LLM feature in production?
Build an evaluation set around actual user tasks, expected outcomes, and known failure cases. Evaluate the end-to-end workflow, not just a model in isolation: retrieval, prompts, authorization, tools, and downstream APIs can all affect the result. Google’s evaluation guidance supports programmatic evaluation and tracking prompt, component, and model versions; it does not prescribe one universal metric set.
Rank #4
Choose measures that match the product risk and use case. Grounding may matter when answers rely on retrieved sources; task success may matter when users need to complete a workflow; safety, latency, and cost may matter where the feature affects sensitive actions or a time-critical user experience. These are possible evaluation dimensions, not a universal required scorecard.
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- Define expected behavior. Record what counts as a useful answer or safe refusal for each case. Where the task depends on retrieved information, include what the answer should be grounded in.
- Run comparisons when components change. Test prompt, retrieval, model, and downstream component changes against the same cases so regressions are visible.
- Record versions with results. Track the prompt, model, and relevant component versions so an observed change can be traced to the system configuration that produced it.
- Review real failures. Use production feedback and incident review to add cases that expose gaps in the evaluation set, while respecting your data-handling and access policies.
A model benchmark is not a substitute for product evaluation: it cannot by itself establish that the right tenant data was retrieved, that a tool call was permitted, or that the whole feature achieved its intended user outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What security practices belong in the development lifecycle?
Threat-model the application around its data flows and authorities: user input, retrieved documents, model responses, tool calls, and downstream services. NIST SP 800-218A, published July 26, 2024, augments the Secure Software Development Framework with generative-AI-specific practices and is intended for model producers, AI-system producers, and acquirers. It is a secure-development reference to use alongside application-specific threat modeling, not a substitute for it.
Make robustness testing ongoing rather than a release-day check. Sanitize and validate external and user input in application code, exercise hostile and malformed cases, and revisit the threat model as prompts, data sources, tools, and models change. OWASP’s RAG guidance is useful for considering risks from ingestion through downstream agents; the NIST framework provides a broader secure-development lens.
Quick Recap
Common implementation failures and what to check
- Answers expose another tenant’s data: inspect authorization at the retrieval and data-service boundaries. The model should not select the tenant scope or determine which fields the user may access.
- The answer follows instructions embedded in a document: treat retrieved text as untrusted, review ingestion and retrieval controls, and verify that tools and output validation prevent the text from authorizing an action.
- A model response triggers an unintended operation: check whether the application validates tool names and parameters against an allowlist and schema before execution. Do not execute free-form model output directly.
- Quality changes after a release: compare the full workflow against the evaluation set and inspect recorded prompt, model, and component versions. A retrieval or downstream change can affect behavior too.
- Evaluation looks good but users still fail the task: check whether the test set reflects real user requests and whether it exercises retrieval, permissions, and downstream APIs rather than only the model response.
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