Evaluating Long-Term Performance of LLM Chatbots

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

LLMs are optimized for next turn response. This results in poor Human-AI collaboration, as it doesn't help users achieve their goals or clarify intent. A new model CollabLLM is optimized for long-term collaboration. The paper "CollabLLM: From Passive Responders to Active Collaborators" by Stanford University and Microsoft researchers tests this approach to improving outcomes from LLM interaction. (link in comments) 💡 CollabLLM transforms AI from passive responders to active collaborators. Traditional LLMs focus on single-turn responses, often missing user intent and leading to inefficient conversations. CollabLLM introduces a :"Multiturn-aware reward" system, apply reinforcement fine-tuning on these rewards. This enables AI to engage in deeper, more interactive exchanges by actively uncovering user intent and guiding users toward their goals. 🔄 Multiturn-aware rewards optimize long-term collaboration. Unlike standard reinforcement learning that prioritizes immediate responses, CollabLLM uses forward sampling - simulating potential conversations - to estimate the long-term value of interactions. This approach improves interactivity by 46.3% and enhances task performance by 18.5%, making conversations more productive and user-centered. 📊 CollabLLM outperforms traditional models in complex tasks. In document editing, coding assistance, and math problem-solving, CollabLLM increases user satisfaction by 17.6% and reduces time spent by 10.4%. It ensures that AI-generated content aligns with user expectations through dynamic feedback loops. 🤝 Proactive intent discovery leads to better responses. Unlike standard LLMs that assume user needs, CollabLLM asks clarifying questions before responding, leading to more accurate and relevant answers. This results in higher-quality output and a smoother user experience. 🚀 CollabLLM generalizes well across different domains. Tested on the Abg-CoQA conversational QA benchmark, CollabLLM proactively asked clarifying questions 52.8% of the time, compared to just 15.4% for GPT-4o. This demonstrates its ability to handle ambiguous queries effectively, making it more adaptable to real-world scenarios. 🔬 Real-world studies confirm efficiency and engagement gains. A 201-person user study showed that CollabLLM-generated documents received higher quality ratings (8.50/10) and sustained higher engagement over multiple turns, unlike baseline models, which saw declining satisfaction in longer conversations. It is time to move beyond the single-step LLM responses that we have been used to, to interactions that lead to where we want to go. This is a useful advance to better human-AI collaboration. It's a critical topic, I'll be sharing a lot more on how we can get there.

Your unit tests mean nothing for LLM features. assert output == expected That line of code — the foundation of every software test you’ve ever written — is useless the moment your system produces non-deterministic output. And most teams shipping AI features right now have no idea what to replace it with. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ December 2023. A Chevrolet dealership in California deployed a GPT-4-powered customer service chatbot on their website. Within days, users had prompt-engineered it into agreeing to sell a 2024 Chevy Tahoe — a $58,000 vehicle — for $1. The bot said, and I quote: “that’s a legally binding offer — no takesies backsies.” The screenshots went viral. The model was doing exactly what a poorly evaluated chatbot does: it had no output guardrails, no adversarial testing, and no system checking whether its responses made any sense before they reached customers. This is what happens when you ship an LLM feature with no evaluation pipeline. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The most common response from engineers new to LLM work is to reach for BLEU or ROUGE scores. These are the standard NLP metrics — they measure how much the generated text overlaps with a reference answer. They don’t work. Consider these two responses to the same question: Reference: “The server crashed due to a memory leak” Generated: “A memory leak caused the application to go down” These mean the same thing. A human reads both and nods. ROUGE gives the second one a score of 0.22 — nearly zero — because the words don’t overlap. The metric is measuring the wrong thing entirely. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ What actually works: a three-layer stack. Layer 1 — Deterministic checks. Free, fast, CI-friendly. Does the response refuse when it shouldn’t? Is the JSON valid? Is it hallucinating URLs? These run in milliseconds on every PR. They catch structural failures before anything else. Layer 2 — LLM-as-judge. This sounds circular. You’re using an AI to evaluate an AI. But it works because evaluation is easier than generation. Use pairwise comparison instead of a 1-5 scale — “which response is better, A or B” — and validate that the judge agrees with humans on 50-100 examples before you trust it. Layer 3 — Human review on 2% of traffic. Expensive. Focused on the queries that the automated layers flag as low confidence. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The brutal truth: Every prompt change you ship is a regression test you didn’t run. LLM systems fail silently. Your monitoring shows 200 OK and 120ms latency. Meanwhile the model has quietly started refusing queries it handled fine last week. You don’t find out until a user complains. The teams getting this right treat their eval dataset as a first-class artifact alongside their code. Full article — the full three-layer implementation, prompt regression testing in CI Link in comments ↓ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ #SystemDesign #AIEngineering #LLM #MachineLearning

“Don’t bet against the models” is the classic advice. And I believe it. But our latest paper revealed there’s still a long way to go before models excel at large context recall. The specific problem is the “needle in a haystack” scenario, where models have to dig through a large context window to find a small bit of information. Historically, this has been a tough problem to solve. Many papers find that: 1. Information buried in the middle of a context window is usually missed, with accuracy dipping from 75% to below 55% in some tests ("Lost in the Middle: How Language Models Use Long Contexts") 2. Conflicting information with training data lowers performance (up to 30% in tests from "LLM In-Context Recall is Prompt Dependent") 3. Performance does improve with state-of-the-art models IF the prompt is favorable ("LLM In-Context Recall is Prompt Dependent") 4. Performance often depends on prompt construction ("LLM In-Context Recall is Prompt Dependent") Which paints a bad picture: agent recall is dependent on luck, a ton of prompt crafting, and hoping that your new data doesn’t conflict with training data. And out of the box, even frontier models have a tough time with this if the stars don’t align. We can do better. In our case, we evaluate recall using the LongMem evaluation, which is representative of the complexity of enterprise GenAI use cases. The eval tests recall of small, nuanced details from a large chat history, such as a customer support conversation. The eval is a real LLM stress test. One thing became abundantly clear from our benchmarking: Zep scored far higher than having entire conversations in the context window. Specifically: - 18.5% aggregate accuracy improvement over the full-context baseline, and 100%+ for many individual tests - A 90% reduction in latency vs. full-context - Only uses 2% of the tokens compared to context stuffing It makes sense - Zep focuses on including only the most relevant information in the context window, instead of dumping everything. So “needle-in-a-haystack” becomes a non-issue. The LLM doesn’t have to sift through everything to find the relevant context - Zep surfaces it automatically. As for “don’t bet against the models”, we’ve clearly got a long way to go. And even if they get better and better at recall, without significant LLM architectural advances, filling the context window will still be slow and expensive. If accuracy and latency are your concern, I wouldn’t wait around!

In the rapidly evolving world of conversational AI, Large Language Model (LLM) based chatbots have become indispensable across industries, powering everything from customer support to virtual assistants. However, evaluating their effectiveness is no simple task, as human language is inherently complex, ambiguous, and context-dependent. In a recent blog post, Microsoft's Data Science team outlined key performance metrics designed to assess chatbot performance comprehensively. Chatbot evaluation can be broadly categorized into two key areas: search performance and LLM-specific metrics. On the search front, one critical factor is retrieval stability, which ensures that slight variations in user input do not drastically change the chatbot's search results. Another vital aspect is search relevance, which can be measured through multiple approaches, such as comparing chatbot responses against a ground truth dataset or conducting A/B tests to evaluate how well the retrieved information aligns with user intent. Beyond search performance, chatbot evaluation must also account for LLM-specific metrics, which focus on how well the model generates responses. These include: - Task Completion: Measures the chatbot's ability to accurately interpret and fulfill user requests. A high-performing chatbot should successfully execute tasks, such as setting reminders or providing step-by-step instructions. - Intelligence: Assesses coherence, contextual awareness, and the depth of responses. A chatbot should go beyond surface-level answers and demonstrate reasoning and adaptability. - Relevance: Evaluate whether the chatbot’s responses are appropriate, clear, and aligned with user expectations in terms of tone, clarity, and courtesy. - Hallucination: Ensures that the chatbot’s responses are factually accurate and grounded in reliable data, minimizing misinformation and misleading statements. Effectively evaluating LLM-based chatbots requires a holistic, multi-dimensional approach that integrates search performance and LLM-generated response quality. By considering these diverse metrics, developers can refine chatbot behavior, enhance user interactions, and build AI-driven conversational systems that are not only intelligent but also reliable and trustworthy. #DataScience #MachineLearning #LLM #Evaluation #Metrics #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gAC8eXmy

🚨 Why do we need to move beyond single-turn task evaluation of large language models (LLMs)? 🤔 I have long advocated for evaluation methods of LLMs and other GenAI applications in healthcare that reflect real clinical scenarios, rather than multiple-choice questions or clinical vignettes with medical jargon. For example, interactions between clinicians and patients typically involve multi-turn conversations. 🔬 A study by Microsoft and Salesforce tested 200,000 AI conversations, using large-scale simulation experiments to compare LLM performance in single- and multi-turn settings. They selected a total of 15 LLMs from eight model families: OpenAI (GPT-4o-mini, GPT-4o, o3, and GPT-4.1), Anthropic (Claude 3 Haiku, Claude 3.7 Sonnet), Google’s Gemini (Gemini 2.5 Flash, Gemini 2.5 Pro), Meta’s Llama (Llama3.1-8B-Instruct, Llama3.3-70B-Instruct, Llama 4 Scout), AI2 OLMo-2-13B, Microsoft Phi-4, Deepseek-R1, and Cohere Command-A. ❓ The results? ❌ Multi-turn conversations resulted in an average 39% drop in performance across six generation tasks. ❌ Their analysis of conversations revealed a minor decline in aptitude and a significant increase in unreliability. 📉 Here's why LLMs stumble: • 🚧 Premature assumptions derail conversations. • 🗣️ Overly verbose replies confuse rather than clarify. • 🔄 Difficulty adapting after initial mistakes. 😵💫 Simply put: When an AI goes off track early, it gets lost and does not recover. ✅ The authors advocate: • Multi-turn conversations must become a priority. • Better multi-turn testing is crucial. Single-turn tests just aren’t realistic. • Users should be aware of these limitations. 🔗 to the original paper is in the first comment 👇 #AI #ConversationalAI #LargeLanguageModels #LLMs