LLM development challenges & how observability helps

Large language models (LLMs) offer unprecedented capabilities for creating innovative features and products, from sophisticated chatbots to automated content generation. However, integrating these powerful models into real-world applications presents unique hurdles for development teams. Unlike traditional deterministic software, LLMs introduce complexities related to performance variability, unpredictable costs, potential inaccuracies, and debugging challenges inherent in their probabilistic nature.

Developers might find themselves asking: Why is the application suddenly slow? Why did the monthly LLM API bill spike unexpectedly? Why did the LLM provide an inaccurate or nonsensical response to a specific user query? Tracking down the root cause of these issues within the complex interplay of application code, prompt engineering, and the LLM itself can be difficult and time-consuming without the right tools.

In this article, you will learn about the top challenges developers encounter when building products with LLMs and explore how implementing robust observability practices can provide the necessary visibility to overcome them effectively.

Understanding the unique challenges of LLM development

Building applications with LLMs differs significantly from traditional software development. Key distinctions include:

• Non-determinism: For the same input prompt, an LLM might produce slightly different outputs, especially with higher "temperature" settings designed for creativity. This makes testing and debugging less predictable.
• Stateful complexity (context window): LLM responses heavily depend on the preceding conversation or provided context. Managing this context window efficiently and correctly is crucial but complex.
• Black box nature: The internal decision-making process of large foundation models is opaque, making it hard to pinpoint why a specific output was generated.
• External dependency: Many applications rely on third-party LLM APIs, introducing external latency, cost factors, and potential points of failure outside the team's direct control.
• Rapid evolution: The LLM landscape changes quickly with new models, techniques, and updated APIs, requiring continuous adaptation.

These unique characteristics lead to specific challenges during development, deployment, and maintenance.

Key LLM development challenges developers face

Integrating LLMs requires careful consideration of several potential obstacles. Let's examine the most common ones.

Managing performance and latency

LLM inference, particularly for complex prompts or large models, can be computationally intensive and take time. Users expect responsive applications, and delays in generating text can significantly degrade the user experience.

Challenge: Identifying bottlenecks is difficult. Is the latency originating from the application's internal processing, the network call to the LLM API, the LLM's inference time itself, or post-processing of the response?

Observability solution:

• Distributed tracing: Track time spent in each request component (prompt construction, LLM API, response processing).
• Metrics: Monitor average and percentile latency, time-to-first-token, tokens per second, and error rates. For self-hosted setups, include GPU/CPU usage and network I/O.

Controlling costs and token usage

Most commercial LLM APIs charge based on input and output tokens. Unoptimized prompts or context windows can quickly escalate costs.

Challenge: It’s difficult to pinpoint what drives token usage or why costs are spiking.

Observability solution:

• Logging: Record token counts per request, feature, or user (excluding PII).
• Metrics: Visualize trends in usage and cost, set alerts for spikes, and analyze average tokens per request.

Ensuring accuracy and reducing hallucinations

LLMs can generate inaccurate or nonsensical outputs (hallucinations), or even biased or inappropriate content.

Challenge: Debugging why a particular answer was incorrect is hard—was it the prompt, the context, or the model?

Observability solution:

• Logging: Capture prompt-response pairs and user feedback.
• Tracing: Reconstruct full context used during LLM calls.
• Metrics: Track downvotes, moderation flags, and sentiment scores to monitor output quality.

Addressing security and privacy concerns

LLMs pose risks around PII leakage and prompt injection attacks.

Challenge: Ensuring user data is handled securely and guarding against manipulation attempts is critical.

Observability solution:

• Logging: Capture metadata (timestamps, sizes, model ID) without sensitive data unless needed and secured.
• Tracing: Map data flow and spot potential leakage points.
• Monitoring: Detect suspicious patterns or malicious input (e.g., scripting attempts), flag PII, and track security anomalies.

Debugging non-deterministic behavior

LLM outputs are probabilistic, meaning the same prompt can yield different results over time.

Challenge: Reproducing bugs is hard without knowing the exact input, parameters, and model used.

Observability solution:

• Logging: Store model settings, prompt, output, and interaction ID.
• Tracing: Link upstream and downstream actions tied to the LLM call for full context reconstruction.

Evaluating output quality consistently

What defines a “good” output varies by application, making evaluation difficult at scale.

Challenge: Quality degrades silently without reliable indicators. Manual reviews don’t scale.

Observability solution:

• Logging: Record user ratings and automated scoring metrics.
• Metrics: Track acceptance rates, quality score trends, and compare model/prompt performance over time.

How observability provides solutions for LLM challenges

Observability, encompassing logging, tracing, and metrics, offers a systematic approach to understanding and managing the complexities of LLM-powered applications.

Leveraging distributed tracing

Function: Break down each request into spans (e.g., user input, DB query, prompt generation, LLM call), linked as one trace.

LLM benefits:

• Identify performance bottlenecks (LLM vs. network vs. backend).
• Spot failures or errors in request chains.
• Understand data transformations and dependencies across microservices.
• Gain end-to-end visibility into LLM interaction workflows.

Utilizing logs for insight and debugging

Function: Record events like prompts, responses, tokens used, model parameters, and errors.

LLM benefits:

• Debug by inspecting exact inputs and responses.
• Attribute and optimize token usage.
• Monitor response quality via feedback and automated scores.
• Enable security audits and compliance reviews.
• Identify common failure patterns through aggregation.

Monitoring metrics for trends and alerts

Function: Track and aggregate performance, cost, and quality over time.

LLM benefits:

• Monitor latency, throughput, and error rates.
• Track token usage and costs per feature or model.
• Observe trends in quality and satisfaction scores.
• For self-hosted models, monitor GPU/CPU utilization and availability.

Implementing LLM observability: Best practices

• Instrument early: Don’t wait for problems to start logging and tracing.
• Define key indicators: Focus on metrics tied to your product’s goals—speed, accuracy, cost, or safety.
• Correlate data: Link traces, logs, and metrics using shared IDs for full context.
• Protect sensitive data: Scrub or avoid logging PII. Apply compliance measures where needed.
• Integrate holistically: Combine LLM observability with your app’s existing observability stack.
• Iterate continuously: Refine your prompts, model usage, and monitoring based on real insights.

Conclusion

Developing applications with Large Language Models (LLMs) presents exciting opportunities but also significant challenges. The unpredictability of LLM behavior, performance, and costs requires advanced observability practices to ensure smooth operations. Without proper visibility, issues like performance bottlenecks, high costs, and inaccuracies can be hard to identify and fix.

Site24x7 offers a comprehensive observability solution to LLM applications. Its advanced APM capabilities help you track API performance, monitor token usage, and diagnose latency issues. With distributed tracing and metrics, Site24x7 gives you the insights needed to pinpoint performance issues and optimize costs, ensuring your LLM-powered applications run efficiently.

Additionally, Site24x7 enables precise logging and analytics to track response accuracy and mitigate issues like hallucinations or bias. By correlating logs, traces, and metrics, Site24x7 ensures you have full visibility into every interaction with your LLM.