Backend Playbook – Cloud & Backend Engineering Tutorials

Quick Summary: Large language models (LLMs) such as ChatGPT are usually not run inside your own backend. Instead, your backend calls them through APIs. This post explains how that integration works, the common patterns, and what backend engineers need to think about in real systems.

Table of Contents

• What Is an LLM in a Backend Context
• Basic Request Flow with ChatGPT APIs
• Common Architecture Patterns
• Handling Context and Conversation State
• Handling Errors, Latency, and Timeouts
• Security and API Key Management
• Cost, Rate Limits, and Observability
• When to Use LLMs in Your Backend
• Final Thoughts

What Is an LLM in a Backend Context

Large language models such as ChatGPT are powerful text processors that can generate, transform, and understand natural language. In a backend system, an LLM is usually treated as an external service that your application calls over HTTPS, similar to calling a payment gateway or email provider.

You do not host the core model weights in your own service. Instead, your backend sends a request to an LLM provider API with inputs such as user messages, instructions, and optional context. The LLM returns a response that your backend post processes and sends to the client.

Basic Request Flow with ChatGPT APIs

At a high level, the request flow for integrating an LLM like ChatGPT into a backend looks like this:

1. The client sends a request to your backend, for example a chat message or query.
2. Your backend validates input, checks authentication, and prepares a prompt or message list.
3. The backend calls the LLM provider API with the prepared payload.
4. The LLM processes the request and returns a generated response.
5. Your backend may apply additional logic, filtering, or formatting.
6. The final response is sent back to the client.

From the backend perspective, this is just another external API call. However, the payloads, latency, error patterns, and safety considerations are different from typical REST integrations.

Common Architecture Patterns

There are several common ways to integrate LLMs into backend systems. Some of the most widely used patterns include:

1. Direct Synchronous Call from Backend

In this pattern, the client makes a request to your backend and your backend directly calls the LLM API in the same request cycle. Once the LLM responds, the backend returns the answer to the client.

This approach is simple and works well for low to moderate traffic where slightly higher latency is acceptable. It is common in early prototypes, internal tools, and low volume applications.

2. Async Job and Callback or Polling

For workloads that are long running or latency sensitive on the frontend, you can push the LLM work to a background job. The backend enqueues a job with the input data, returns a job identifier to the client, and the client either polls for results or uses a callback or WebSocket channel.

This pattern decouples user experience from LLM response time and is useful for document processing, large batch queries, and workflows where waiting several seconds is acceptable as long as the UI remains responsive.

3. Microservice Dedicated to AI

Many teams create a dedicated AI or inference microservice. Other services call this microservice instead of talking directly to the LLM provider. The AI service centralizes prompt templates, safety checks, request logging, and provider specific logic.

This architecture makes it easier to switch providers, A/B test models, and enforce consistent behavior across multiple products.

Handling Context and Conversation State

Unlike simple request response APIs, LLMs often need conversation history or additional context to respond accurately. Backend engineers must decide where and how to store that context.

Common strategies include:

• Storing conversation messages in a database and sending a window of recent messages to the LLM.
• Summarizing long histories to keep prompts within token limits.
• Using vector databases and retrieval techniques to provide relevant documents to the model.

The backend is responsible for trimming, summarizing, and enriching context before calling the LLM API. Good context management often matters more than the specific model version.

Handling Errors, Latency, and Timeouts

LLM calls can be slower and more variable than typical REST APIs. Backend systems should treat LLMs as potentially slow and occasionally failing dependencies.

Practical considerations include:

• Setting sensible timeouts for outbound API calls.
• Implementing retries with backoff for transient failures.
• Returning graceful fallbacks if the LLM is unavailable.
• Using streaming responses when available to improve perceived latency.

These patterns are similar to other distributed systems concerns, but latency budgets and expectations must be carefully managed for user facing experiences.

Security and API Key Management

All calls to an LLM provider such as OpenAI are authenticated using an API key or similar credential. The backend should never expose this key to the frontend. Instead, the backend acts as a trusted middle layer.

Best practices include:

• Storing API keys in secure secrets management systems.
• Restricting which services can access the keys.
• Validating and sanitizing user input before sending it to the model.
• Filtering or redacting sensitive data to avoid sending unnecessary information.

Some workloads also require additional safety checks on model outputs before they reach end users, especially in public facing applications.

Cost, Rate Limits, and Observability

LLM APIs are usually billed based on tokens or usage. From a backend perspective, each call has both a performance cost and a financial cost. Tracking and controlling both is important.

Typical techniques:

• Logging prompt and response token counts per request.
• Aggregating usage per user, per feature, or per tenant.
• Implementing rate limits or quotas at the backend layer.
• Using caching for repeated or deterministic queries when appropriate.

Good observability helps teams detect misuse, optimize prompts, and keep costs under control while maintaining a good experience.

When to Use LLMs in Your Backend

LLMs are a good fit when your backend needs to work with unstructured language: summarizing text, extracting structured data from natural language, assisting users in writing content, answering questions over documentation, or powering chat style experiences.

They are less suitable when strict determinism, hard real time constraints, or guaranteed correctness are required. In those cases, LLMs may still be used as helpers, but final decisions should rely on traditional business logic and validation.

Final Thoughts

For backend engineers, LLMs such as ChatGPT are powerful new building blocks that behave like intelligent external services. The core skills remain the same: design clear interfaces, manage dependencies, handle failures, and monitor performance and cost.

Future posts will explore concrete patterns such as retrieval augmented generation, prompt design from the backend perspective, and how to combine traditional microservices with AI powered capabilities.

Next up: Read our introduction to backend engineering and system design for more context on building modern backend systems.