The Complete Guide to Debugging LLM Applications: Methods, Tools, and Solutions

Yusuf Ishola· May 21, 2025

Building LLM applications is fun and exciting...until something breaks.

When things break—and they will—you need effective ways to identify and fix issues.

This guide covers everything you need to know about debugging LLM applications, we'll explain the unique challenges of debugging LLM applications and provide practical solutions using modern observability tools like Helicone.

Table of Contents

• Understanding the Unique Challenges of LLM Debugging
• Essential Tools for Effective LLM Debugging
• Step-by-Step Guide to Debugging Common LLM Issues
• Advanced Debugging Techniques
• LLM Observability: The Backbone of effective LLM Debugging
• Conclusion
• Further Resources

Understanding the Unique Challenges of LLM Debugging

We hear the same story from nearly every team: 'We shipped a broken tool call chain to prod. It took us 3 hours to trace the root cause.' — Team Helicone

Without proper observability, debugging LLM applications is a nightmare.

Debugging LLM applications is fundamentally different from traditional software debugging. Here's why:

Non-deterministic Behavior

Unlike traditional software where the same input reliably produces the same output, LLMs can generate different responses to identical prompts. This non-deterministic nature makes reproducing and fixing bugs significantly more difficult.

Multi-step Workflows

Modern LLM applications rarely consist of a single model call. They often involve complex chains of multiple LLM calls, tool and API integrations, RAG (Retrieval-Augmented Generation) systems, and vector database queries.

A failure at any point in this chain can cascade through the entire system, making it hard to pinpoint the source of issues.

Common LLM Failure Modes

LLM applications experience unique forms of failure that traditional debugging tools aren't designed to handle:

1. Hallucinations: When models confidently generate factually incorrect information
2. Prompt Injection Attacks: When malicious inputs manipulate model behavior
3. Format Errors: When outputs don't match expected formats (JSON, code, etc.)
4. RAG Failures: When retrieval doesn't return relevant context
5. Token Limit Overflows: When inputs exceed model context windows
6. Tool Call Errors: When function calling fails due to schema mismatches
7. Data Leakage: When sensitive data is leaked to unintended recipients

Signs Your LLM Application Needs Better Debugging

You might need to improve your debugging approach if you're experiencing:

• Unexplained cost spikes with no corresponding traffic increase
• Users reporting incorrect or nonsensical responses
• System failures that are difficult to reproduce
• Slow response times in production
• Difficulty determining which prompt changes are effective

Essential Tools for Effective LLM Debugging

Comprehensive Request Logging

The foundation of LLM debugging is thorough logging of Input prompts, Generated responses, Metadata (timestamps, model versions, etc.), Token usage and costs, and Latency metrics.

With Helicone, you can implement this with minimal code changes:

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://oai.helicone.ai/v1",
  defaultHeaders: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
  },
});

This single integration—perhaps the most impactful step in your debugging journey—gives you immediate visibility into all your LLM calls without modifying your existing code structure.

Multi-step (Agentic) Workflow Tracing

For complex applications, you need to trace entire user journeys across multiple LLM calls. Helicone's Sessions feature enables this with just a few additional headers:

Helicone's Sessions also gives you visibility into tool usage, vector database queries, and external API calls.

const sessionId = randomUUID();

openai.chat.completions.create(
  {
    messages: [
      {
        role: "user",
        content: "Generate an abstract for a course on space.",
      },
    ],
    model: "gpt-4",
  },
  {
    headers: {
      "Helicone-Session-Id": sessionId,
      "Helicone-Session-Path": "/abstract",
      "Helicone-Session-Name": "Course Planning Workflow",
    },
  }
);

Step-by-Step Guide to Debugging Common LLM Issues

Debugging Hallucinations

Hallucinations occur when LLMs generate factually incorrect or nonsensical information despite appearing confident.

Steps to debug:

1. Identify hallucination patterns: Use Helicone to filter for requests with negative user feedback
2. Check prompt clarity: Examine if the prompt is ambiguous or lacks specific instructions
3. Review RAG inputs: Verify if retrieved context is accurate and relevant
4. Test prompt variations: Use Helicone's Playground to experiment with more precise prompts
5. Implement verification steps: Add explicit fact-checking steps in your application flow

For comprehensive strategies, check out How to Reduce LLM Hallucination in Production Apps.

Debugging Prompt Injection Attacks

Prompt injections occur when malicious users attempt to manipulate your LLM by inserting commands in their inputs.

Steps to debug:

1. Monitor for abnormal behavior: Parse logs and use Helicone's real-time alerts to spot and get notified of unusual response patterns
2. Examine suspicious inputs: Review user prompts that trigger unexpected model behavior and react appropriately
3. Implement security headers: Add Helicone's security feature with a simple header to easily detect and block malicious inputs

client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": user_input}],
    extra_headers={
        "Helicone-LLM-Security-Enabled": "true", # Enable basic security
        "Helicone-LLM-Security-Advanced": "true" # Enable advanced security
    }
)

Learn more in the Developer's Guide to Preventing Prompt Injection.

Debugging Token Limit Issues

Token limit issues occur when inputs exceed the model's context window or when costs unexpectedly increase.

Steps to debug:

1. Track token usage: Use Helicone's built-in token tracking for all requests
2. Identify expensive endpoints: Filter for routes or users consuming excessive tokens
3. Optimize prompt templates: Experiment with more token-efficient prompts
4. Implement chunking strategies: Break large inputs into manageable pieces
5. Set up alerts: Configure notifications for abnormal token usage

Advanced Debugging Techniques

Replaying and Modifying Sessions

For deep debugging, you can replay entire user sessions with modifications to see how changes impact outcomes:

// Create a new session ID for the replay
const replaySessionId = randomUUID();

// Loop through original requests with modifications
for (const request of originalRequests) {
  const modifiedRequest = modifyRequestBody(request);
  
  // Send modified request with the same session structure
  await sendRequest(modifiedRequest, replaySessionId, sessionPath, sessionName);
}

This technique is invaluable for testing prompt improvements on real-world interactions without affecting users. For more details, read Optimizing AI Agents: How Replaying LLM Sessions Enhances Performance.

A/B Testing Prompts

To systematically improve LLM responses, conduct controlled experiments with different prompts:

1. Create multiple prompt variants in Helicone's Experiments feature
2. Test them against the same set of inputs
3. Measure performance metrics (accuracy, latency, cost)
4. Choose the best-performing variant for production

Check out the Prompt Evaluation Explained guide to learn about random sampling vs. golden datasets for evaluation.

Automated LLM Evaluation

Set up automated evaluation pipelines to continuously monitor LLM output quality:

1. Define quality metrics relevant to your application (accuracy, helpfulness, safety)
2. Implement evaluators (LLM-as-judge, heuristic rules, user feedback)
3. Score responses automatically
4. Track quality trends over time

Learn more about tracking LLM user feedback to improve your AI applications.

Build a Debugging-Friendly LLM Architecture

Design your application architecture with debugging in mind:

1. Add custom properties: Tag requests with metadata for better filtering
2. Track user IDs: Associate requests with specific users for easier troubleshooting
3. Implement caching for greater reliability and cost control
4. Implement rate limiting: Protect your application from overuse and unexpected costs with custom rate limits

Learn more about user metrics in Helicone.

LLM Observability: The Backbone of effective LLM Debugging

As LLM applications continue to evolve, the importance of comprehensive observability will only grow. The five essential pillars of LLM observability can help create a strong base for effective debugging:

1. Traces & Spans: Detailed logging of request/response pairs and multi-step interactions
2. LLM Evaluation: Measuring output quality against specific criteria
3. Prompt Engineering: Managing and optimizing prompts systematically
4. Search and Retrieval: Monitoring and improving RAG effectiveness
5. LLM Security: Protecting against vulnerabilities and misuse

Learn how to implement LLM observability for production to put these principles into practice.

Conclusion

Effective debugging is the difference between an AI application that delights users and one that frustrates them. By implementing the strategies in this guide and leveraging modern observability tools like Helicone, you can build more reliable, cost-effective, and user-friendly LLM applications.

Remember that LLM debugging is an ongoing process. As models evolve and applications grow more complex, your debugging approaches should evolve too. The investment you make in observability today will pay dividends in better user experiences and easier maintenance tomorrow.

Further Resources

• 6 Best Practices for Building Reliable AI Applications
• The Ultimate Guide to Effective Prompt Management
• How to Test Your LLM Prompts
• Top Prompt Evaluation Frameworks
• Debugging Features in Helicone