LLMs have fundamentally changed how we build software, but many teams are still applying traditional testing approaches that fall short. Unlike conventional applications where we can predict outputs and define clear pass/fail criteria, LLMs are probabilistic systems that generate novel responses. This means testing strategies need to evolve based on how deterministic or open-ended your LLM application is. If you're relying solely on accuracy metrics and pre-launch testing, you're likely missing critical failure modes that will only surface in production. Here are three warning signs that your LLM testing strategy needs more depth.

Red Flag #1: You're Only Testing Happy Paths

The Problem

Happy path testing for LLMs typically involves feeding the model straightforward, well-formed prompts that represent ideal use cases. You might test a customer service chatbot with questions like "What are your business hours?" or "How do I reset my password?" These prompts are clear, polite, and exactly the kind of interaction you hope users will have. Your model answers correctly, your tests pass, and everything looks great.

But this approach is dangerously insufficient. Real users don't always ask questions politely or clearly. They make typos, they get frustrated, they try to trick the system, and sometimes they actively attempt to break it. Edge cases multiply rapidly with LLMs because the input space is essentially infinite. Unlike a traditional form with dropdown menus and validation rules, your LLM accepts any text a user can type. This opens the door to adversarial inputs, prompt injection attacks where users try to override your system instructions, and jailbreak attempts that aim to bypass safety guardrails. If you haven't explicitly tested for these scenarios, you have no idea how your model will behave when they inevitably occur in production.

What You're Missing

When you only test happy paths, entire categories of failure modes remain invisible. Boundary testing for ambiguous inputs is critical because users often ask vague or poorly structured questions. How does your chatbot handle "that thing from before" or "you know what I mean"? Can it gracefully ask for clarification, or does it hallucinate an answer?

Multilingual and code-switched text presents another challenge. Users might start a sentence in English and finish in Spanish, or use transliterated text from non-Latin scripts. Your model needs to handle these scenarios without breaking or producing nonsensical responses. Similarly, responses to intentionally confusing or contradictory prompts reveal whether your system can maintain coherence under pressure. What happens when someone asks "Is your company the best or the worst?" or provides conflicting information within a single query?

Perhaps most importantly for real-world deployment, you need to test robustness to typos, slang, and unconventional phrasing. Users don't proofread casual messages. They write "ur" instead of "your," they use regional slang, and they structure sentences in ways that would make an English teacher cringe. If your testing data consists only of grammatically perfect, professionally written text, you're setting yourself up for failure.

The Fix

The solution starts with red team testing and adversarial evaluation. Dedicate time to actively trying to break your system. Have team members attempt prompt injections, test offensive inputs, and explore edge cases deliberately. This adversarial mindset uncovers vulnerabilities that polite testing never will.

Build a diverse test set that explicitly includes edge cases across multiple dimensions: linguistic variety, formatting irregularities, ambiguous phrasing, and adversarial attempts. Your test coverage should reflect the messy reality of human communication, not an idealized version of it. Document the edge cases you discover in production and feed them back into your test suite continuously.

Finally, test for graceful degradation. Your LLM will fail sometimes, that's inevitable. The question is how it fails. Does it admit uncertainty when appropriate? Does it maintain safety guardrails even under pressure? Does it avoid hallucinating confident answers to nonsensical questions? A system that fails gracefully is far more trustworthy than one that appears perfect in testing but crashes spectacularly when users get creative.

Red Flag #2: You're Treating All Outputs as Pass/Fail

The Problem

Many teams evaluate LLM outputs the same way they would test traditional software: the system either gives an answer or it doesn't, and that answer either matches the expected output or it fails. This binary thinking might work for a ticket classification system where you need exactly "low," "medium," or "high," but it completely misses the nuance of generative AI quality.

Consider a customer service chatbot that answers a refund question with technically accurate information but delivers it in a curt, almost hostile tone. Or one that provides a correct answer buried within five paragraphs of unnecessary context. Maybe your chatbot consistently uses formal corporate language when your brand voice is casual and friendly. In all these cases, a simple pass/fail test would mark these as successes because the factual content is correct. But from a user experience perspective, and potentially from a business perspective, these responses are failures.

This approach also assumes that there's a single "correct" answer to compare against, which fundamentally misunderstands how generative models work. Unlike deterministic systems, LLMs can produce many different valid responses to the same prompt. Treating evaluation as a binary choice between matching a reference answer or failing means you'll either get false negatives when good responses don't match your template exactly, or you'll miss quality issues when responses technically pass but are actually problematic.

What You're Missing

Tone and style consistency matter enormously for user-facing applications. Your LLM might be brilliant in one interaction and sound like a different personality entirely in the next. It might match your brand voice perfectly for simple questions but drift into overly formal or casual language when handling complex queries. Without evaluating tone explicitly, you won't catch these inconsistencies until users complain about the jarring experience.

Hallucinations represent one of the most dangerous failure modes for LLMs. These are responses that sound authoritative and well-structured but contain factually incorrect information. A binary pass/fail approach often misses hallucinations entirely because the response looks good on the surface. The model confidently states incorrect dates, invents nonexistent features, or fabricates policy details. Without specific evaluation for factual accuracy, these confident lies slip through testing.

Relevance and helpfulness extend beyond mere correctness. An answer can be technically accurate but completely miss what the user actually needed to know. Maybe your chatbot answers the literal question while ignoring the underlying intent, or provides correct information that's not actionable, or fails to anticipate obvious follow-up needs. These nuances disappear in binary evaluation.

Safety and bias issues are perhaps the most critical oversight. An LLM might provide helpful answers while occasionally producing responses that are inappropriate, biased, or potentially harmful. Binary testing that only checks for task completion will completely miss these problems. You need explicit evaluation dimensions for toxicity, bias across demographic groups, and adherence to safety guidelines.

The Fix

Implement multi-dimensional evaluation that treats quality as a composite of several factors: correctness, helpfulness, safety, tone, and relevance. Each dimension should have clear criteria and be evaluated independently. A response might score well on correctness but poorly on tone, and you need visibility into both to make informed decisions about model performance.

Use LLM-as-a-judge approaches for qualitative aspects that are difficult to capture with rules or exact matching. Another language model can evaluate whether a response matches your brand voice, whether it's appropriately empathetic, or whether it successfully addresses the user's underlying need. While not perfect, LLM judges can assess nuanced qualities at scale in ways that lexical matching simply cannot.

Create rubrics that capture your actual business requirements beyond accuracy. What does "good" look like for your specific use case? If you're building a mental health support chatbot, empathy and appropriate crisis handling might matter more than perfect factual recall. If you're building a code assistant, actionability and clarity might trump comprehensiveness. Your evaluation framework should reflect what success actually means for your users and your business.

Finally, track trends over time rather than obsessing over point-in-time pass rates. LLM performance can drift as you update models, modify prompts, or as the distribution of user queries shifts. Set up dashboards that show how your quality dimensions evolve across versions and time periods. Look for degradation patterns, identify which dimensions are most stable, and catch regressions before they compound. A single test run tells you almost nothing; trends tell you everything.

Red Flag #3: You Have No Visibility Into Production Behavior

The Problem

Testing in development or staging environments with carefully curated synthetic data gives you a false sense of security. Your test suite might cover hundreds of scenarios that you and your team brainstormed, but real users are infinitely more creative, unpredictable, and resourceful than any testing team. They approach your LLM with contexts, assumptions, and use cases you never anticipated. They combine features in unusual ways, they misunderstand your interface, and they push boundaries you didn't know existed.

Users will find ways to use and break your LLM that you never imagined. Someone will try to use your customer service chatbot as a therapist. Another will attempt to get your code assistant to write their entire application. A third will discover that asking questions in a specific sequence triggers unexpected behavior. These aren't malicious actors or edge cases; this is normal user behavior when a tool is flexible and powerful. The creativity and diversity of real-world usage patterns dwarf anything you can simulate in testing.

This creates a significant deployment gap between test performance and production performance. Your model might achieve 95% accuracy on your test set but struggle in production because the actual distribution of queries looks nothing like your test data. Maybe your test queries are shorter and more direct than real user questions. Perhaps your synthetic data lacks the emotional urgency or frustration that colors real customer service interactions. The problem isn't that your testing was bad; it's that testing alone, without production visibility, can never capture the full reality of how your system will be used.

What You're Missing

Without production monitoring, you have no idea what users are actually asking. The real-world prompt distribution might be radically different from what you expected. Maybe 40% of queries are about a feature you thought was niche. Perhaps users consistently phrase requests in ways that confuse your model. You might discover that a significant portion of traffic comes during specific events or seasons, with completely different characteristics than your baseline. Understanding what users actually want is impossible from a test suite alone.

Failure patterns that only emerge at scale remain invisible until you're watching production traffic. Rare edge cases become common when you're handling thousands of requests per day. Subtle failure modes that occur in 0.1% of cases might be negligible in testing but translate to dozens of angry users daily in production. Cascading failures, where one bad response leads users down an unrecoverable path, only reveal themselves through real interaction sequences.

Drift over time as user behavior evolves is perhaps the most insidious problem. Your LLM's performance today tells you nothing about its performance next month. Users learn how to phrase questions more effectively, or they start asking about new topics as your product evolves, or they develop workarounds for limitations they've discovered. External events shift the conversation entirely. Without continuous monitoring, you won't notice gradual degradation until it becomes a crisis.

Context-specific issues related to time of day, user demographics, device types, or geographic regions are completely invisible in aggregate testing. Maybe your model performs beautifully for desktop users but struggles with the terser queries from mobile users. Perhaps it works well in English-speaking markets but has subtle problems with regional dialects or cultural references. Your peak traffic hours might correlate with lower quality scores because users are rushed and less patient. These patterns only emerge when you can slice production data along multiple dimensions.

The Fix

Implement comprehensive production monitoring and logging as a non-negotiable component of your LLM deployment. Log prompts, responses, latencies, and any metadata that might be relevant for analysis. Build your logging infrastructure with privacy in mind, but don't let privacy concerns become an excuse for flying blind. You cannot improve what you cannot measure, and you cannot measure what you do not observe.

Use sampling to evaluate real conversations at scale. You cannot manually review every interaction, but you can systematically sample a representative subset. Stratify your sampling to ensure you capture different user segments, time periods, and interaction types. Apply both automated evaluation metrics and human review to these samples. This gives you ground truth about actual performance while remaining operationally feasible.

Build feedback loops that capture signals directly from users and from human operators. Implement rating systems that let users indicate satisfaction with responses. Track when conversations get escalated to human support, as these escalations often indicate model failures. Monitor user behaviors like rephrasing questions multiple times or abandoning conversations, which suggest the model isn't meeting their needs. Create easy pathways for your support team to flag problematic interactions for review.

Create dashboards for key quality metrics that stakeholders can check regularly. Make production performance visible and actionable. Include metrics across all the dimensions you care about: accuracy, safety, latency, user satisfaction, and escalation rates. Break these down by user segment, time period, and conversation type. Set up alerts for anomalies so you catch problems quickly rather than discovering them in monthly reviews.

Establish processes for continuous evaluation and model updates. Production monitoring isn't a one-time project; it's an ongoing practice. Schedule regular reviews of production data to identify new failure modes, emerging use cases, and opportunities for improvement. Feed production failures back into your test suite so you don't regress on known issues. Create a cadence for model updates that balances stability with continuous improvement. Treat your LLM deployment as a living system that requires active maintenance, not a finished product that you ship and forget.

Conclusion

LLM testing requires a fundamental shift from traditional quality assurance practices. If you're only testing happy paths, treating outputs as binary pass/fail, or relying solely on pre-production testing, you're leaving critical gaps in your quality strategy. These aren't nice-to-have improvements; they're essential practices for deploying LLMs responsibly and effectively.

The good news is that you don't need to solve everything at once. Start by addressing whichever red flag feels most urgent for your application. Add some adversarial test cases this week. Introduce a multi-dimensional evaluation rubric next sprint. Set up basic production logging by the end of the month. Each improvement catches issues that would otherwise surface as user complaints or worse. Your future self, and your users, will thank you for investing in depth over speed when it comes to LLM quality.