Error Handling and Resilience Patterns
Design robust error handling, retry mechanisms, and circuit breakers to make your LLM applications more fault-tolerant.
Error Handling and Resilience Patterns is a free LLM Apps in Production (RAG + Vector DB + Caching) lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LLM Apps in Production (RAG + Vector DB + Caching) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Build Robust LLM Apps
LLM applications, especially those interacting with external APIs, need to be tough!
Resilience is about designing systems that can recover from failures gracefully, without crashing or providing a bad user experience.
In this lesson, we'll learn patterns to make your LLM apps more fault-tolerant.
Typical Failures
What kind of errors can an LLM application face?
- API Rate Limits: Too many requests at once.
- Network Issues: Temporary connection drops.
- LLM Service Unavailability: The LLM provider is down.
- Bad LLM Responses: Model returns invalid JSON or hallucinates.
- Dependency Failures: Vector DB or other services fail.
Standard Try-Catch
The first line of defense is standard error handling using try-catch blocks. This prevents your entire application from crashing when an expected error occurs.
It allows you to log the error, inform the user, or attempt a fallback.
public class Main {
public static void main(String[] args) {
try {
// Simulate an LLM API call that might fail
callLlmApi();
System.out.println("API call successful.");
} catch (Exception e) {
System.out.println("Error: " + e.getMessage());
// Log the error, notify user, etc.
}
}
public static void callLlmApi() throws Exception {
// In a real app, this would make an actual API call
if (Math.random() < 0.5) { // 50% chance of failure
throw new RuntimeException("LLM service unavailable.");
}
}
}Why Just Catching Isn't Enough
Some errors are transient, meaning they're temporary and might resolve if you just try again. Think of a brief network glitch or a momentary rate limit.
A simple try-catch just fails immediately. For transient errors, a retry mechanism can significantly improve reliability without user intervention.
Simple Retry Logic
We can implement a basic retry loop. If an error occurs, we wait a bit and try again, up to a maximum number of attempts.
public class Main {
public static void main(String[] args) {
int maxRetries = 3;
int currentRetry = 0;
boolean success = false;
while (currentRetry < maxRetries && !success) {
try {
System.out.println("Attempt " + (currentRetry + 1));
callLlmApi();
System.out.println("API call successful.");
success = true;
} catch (Exception e) {
System.out.println("Error: " + e.getMessage());
currentRetry++;
if (currentRetry < maxRetries) {
System.out.println("Retrying in 1 second...");
try { Thread.sleep(1000); } catch (InterruptedException ie) {}
}
}
}
if (!success) {
System.out.println("All retries failed.");
}
}
public static void callLlmApi() throws Exception {
// Simulate an LLM API call with 70% chance of failure
if (Math.random() < 0.7) {
throw new RuntimeException("Transient network error.");
}
}
}Smart Retries: Exponential Backoff
Constant retry delays can overwhelm a struggling service. Exponential backoff is a strategy where the delay between retries increases exponentially.
This gives the remote service more time to recover and prevents your app from hammering it with requests.
- Initial delay: 1s
- Second delay: 2s
- Third delay: 4s
- And so on...
Circuit Breaker Pattern
What if a service is truly down, not just experiencing transient errors? Retrying repeatedly only wastes resources and delays failure detection.
The Circuit Breaker pattern prevents an application from repeatedly trying to invoke a service that is likely to fail, saving resources and allowing the service time to recover.
Circuit Breaker States
A circuit breaker has three main states:
- Closed: Operations proceed normally. If errors exceed a threshold, it trips to Open.
- Open: All requests fail immediately without trying the service. After a timeout, it transitions to Half-Open.
- Half-Open: A limited number of requests are allowed to pass through to test if the service has recovered. If successful, it goes back to Closed; otherwise, back to Open.
Preventing Hung Requests with Timeouts
LLM API calls can sometimes hang indefinitely, waiting for a response that never comes. This can exhaust resources and degrade user experience.
Always configure timeouts for your API calls. This sets a maximum duration your application will wait for a response before giving up and throwing an error.
import java.util.concurrent.TimeUnit;
public class Main {
public static void main(String[] args) {
long startTime = System.nanoTime();
long timeoutMillis = 2000; // 2 seconds timeout
try {
System.out.println("Calling LLM API with a timeout...");
callLlmApiWithTimeout(timeoutMillis);
System.out.println("API call completed successfully.");
} catch (Exception e) {
System.out.println("API call failed: " + e.getMessage());
}
long endTime = System.nanoTime();
long duration = TimeUnit.NANOSECONDS.toMillis(endTime - startTime);
System.out.println("Total duration: " + duration + "ms");
}
public static void callLlmApiWithTimeout(long timeoutMillis) throws Exception {
// Simulate a long-running/hung API call
long processingTime = 2500; // 2.5 seconds
if (processingTime > timeoutMillis) {
throw new RuntimeException("Operation timed out after " + timeoutMillis + "ms");
}
try {
Thread.sleep(processingTime);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException("API call interrupted.", e);
}
}
}Resilience Check
When should you use a Circuit Breaker pattern instead of just a Retry mechanism?
Recap: Building Resilient LLM Apps
We've covered key patterns for making your LLM applications fault-tolerant:
- Basic Error Handling: Using
try-catchfor immediate failure management. - Retry Mechanisms: For handling transient errors, often with exponential backoff.
- Circuit Breakers: To prevent overwhelming consistently failing services.
- Timeouts: Essential for preventing hung API calls and resource exhaustion.
These patterns are crucial for robust production LLM systems!
Frequently asked questions
Is the “Error Handling and Resilience Patterns” lesson free?
Yes — the full text of “Error Handling and Resilience Patterns” is free to read here on the web, and the LLM Apps in Production (RAG + Vector DB + Caching) course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LLM Apps in Production (RAG + Vector DB + Caching) course, upgrade to CoddyKit PRO.
What will I learn in “Error Handling and Resilience Patterns”?
Design robust error handling, retry mechanisms, and circuit breakers to make your LLM applications more fault-tolerant. You practise LLM Apps in Production (RAG + Vector DB + Caching) with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start LLM Apps in Production (RAG + Vector DB + Caching)?
No prior experience is required. LLM Apps in Production (RAG + Vector DB + Caching) on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Error Handling and Resilience Patterns” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this LLM Apps in Production (RAG + Vector DB + Caching) lesson?
Yes. Every LLM Apps in Production (RAG + Vector DB + Caching) lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Securing LLM API Keys and Sensitive Data
- Rate Limiting and Abuse Prevention
- Error Handling and Resilience Patterns
- Defending Against Prompt Injection