Common Issues in LM Arena Testing

Imagine you are in a GenAI interview and the interviewer asks you this question on Vector DB Interviewer: You are using Chroma as your vector store in a RAG system. During ingestion, you sometimes see errors, and even when ingestion succeeds, retrieval quality varies. What common issues have you seen with Chroma in such setups, and how would you debug and fix them? Explanation: In RAG systems using Chroma, the most common issues fall into three categories: ingestion errors, configuration problems, and retrieval quality issues. 1️⃣ First, ingestion failures often happen due to data formatting issues. Chroma expects properly structured Document objects with consistent metadata. Passing raw strings, malformed lists, or inconsistent IDs can cause silent failures or partial ingestion. To debug this, I first log the number of documents before and after ingestion using collection counts and validate a few stored entries with test queries. 2️⃣ Second, embedding-related issues are very common. Since Chroma relies on external embedding models, missing or misconfigured API keys, model version mismatches, or inconsistent embedding dimensions can halt ingestion or degrade retrieval quality. I verify environment variables, confirm embedding dimensions remain constant, and re-embed data if the embedding model changes. 3️⃣ Third, retrieval quality variation is usually caused by poor chunking strategies or parameter misconfiguration. Large chunks dilute semantic meaning, while very small chunks lose context. Similarly, incorrect top_k values or misunderstanding distance vs similarity thresholds leads to irrelevant results. I debug this by inspecting retrieved chunks, tuning chunk size and overlap, and adjusting retrieval parameters incrementally. Finally, I always manually review retriever outputs before passing them to the LLM. If the retrieved context is weak, the generation will fail regardless of model quality. Monitoring retrieval relevance, running similarity sanity checks, and adding logging around retrieval scores helps stabilize performance. Overall, fixing Chroma issues requires validating ingestion, ensuring embedding consistency, and continuously inspecting retriever outputs #chromadb #llm #rag #vectordb

Multi-agent systems (MASs) using LLMs commonly fail due to structural design flaws rather than limitations of the underlying models. A grounded analysis of over 150 execution traces from five open-source MASs reveals 14 distinct failure modes, grouped into three categories: specification and system design failures, inter-agent misalignment, and task verification and termination failures. These issues include unclear task definitions, agents overstepping roles, poor communication, and inadequate validation of outputs. A taxonomy of these failures is developed through expert annotation with high inter-rater reliability and supported by an automated LLM-based evaluator that achieves strong agreement with human judgments. Efforts to reduce failures through improved prompts and refined agent orchestration show only limited success, indicating that tactical fixes are insufficient. Effective MAS design requires deeper structural strategies, such as rigorous verification, standardized communication protocols, memory management, and organizational principles drawn from high-reliability human systems. https://lnkd.in/ghBww-p7

Some challenges in building LLM-powered applications (including RAG systems) for large companies: 1. Hallucinations are very damaging to the brand. It only takes one for people to lose faith in the tool completely. Contrary to popular belief, RAG doesn't fix hallucinations. 2. Chunking a knowledge base is not straightforward. This leads to poor context retrieval, which leads to bad answers from a model powering a RAG system. 3. As information changes, you also need to change your chunks and embeddings. Depending on the complexity of the information, this can become a nightmare. 4. Models are black boxes. We only have access to modify their inputs (prompts), but it's hard to determine cause-effect when troubleshooting (e.g., Why is "Produce concise answers" working better than "Reply in short sentences"?) 5. Prompts are too brittle. Every new version of a model can cause your previous prompts to stop working. Unfortunately, you don't know why or how to fix them (see #4 above.) 6. It is not yet clear how to reliably evaluate production systems. 7. Costs and latency are still significant issues. The best models out there cost a lot of money and are very slow. Cheap and fast models have very limited applicability. 8. There are not enough qualified people to deal with these issues. I cannot highlight this problem enough. You may encounter one or more of these problems in a project at once. Depending on your requirements, some of these issues may be showstoppers (hallucinating direction instructions for a robot) or simple nuances (support agent hallucinating an incorrect product description.) There's still a lot of work to do until these systems mature to a point where they are viable for most use cases.

“Gen AI is just data retrieval + prompt + API call. Nothing more, nothing less.” Yesterday, I read this comment and was left shocked: What hit harder? It came from a 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. Let’s get this straight: That statement might look accurate at surface level. But 𝗱𝗲𝗲𝗽𝗹𝘆 𝘄𝗿𝗼𝗻𝗴 in everything that matters when building real systems. It’s like saying: “Backend engineering is just request + handler + response.” No mention of auth, rate-limiting, failover, caching, tracing, observability, scaling, or tradeoffs. Just vibes. And this is the problem. LLM-powered apps aren’t trivial glue code. They require 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀, not fewer. Here’s why: 𝟭. 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝘀𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰 Same input ≠ , same output. You’re not programming functions, you’re influencing probability distributions. That means reproducibility, versioning, and debugging become real problems. 𝟮. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗶𝘀𝗻’𝘁 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁 Top-k from a vector DB ≠ “relevant data.” Chunking, reranking, formatting, source prioritisation all affect answer quality. And now your system is doubly stochastic: retrieval + generation. 𝟯. 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 No static types. No modularity. Small changes break output. Long prompts cost latency. Production systems treat prompts like code: version them, test them, and route through orchestrators for multi-step tasks. 𝟰. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗯𝗿𝗼𝗸𝗲𝗻 No .𝚊𝚜𝚜𝚎𝚛𝚝𝙴𝚚𝚞𝚊𝚕() No clear failure paths. Human evals are slow, and heuristic metrics are flawed. “Works on one input” ≠ production-ready. 𝟱. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗶𝘀 𝗮 𝗳𝗶𝗿𝘀𝘁-𝗰𝗹𝗮𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 LLMs are vulnerable to: • Prompt injection • Data leakage • Jailbreaking • Information retrieval attacks • Model exploitation via malformed input If you’re not sanitising inputs and isolating model behaviour, you’re walking into a breach with your eyes open. 𝟲. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗺𝗮𝗻𝗱𝗮𝘁𝗼𝗿𝘆 You won’t get stack traces. You won’t know why it failed. You’ll get an output that “looks okay”, until it’s confidently wrong in production. You need structured logging, tracing, diffing, eval frameworks. 𝟳. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗻𝗼𝗻-𝘁𝗿𝗶𝘃𝗶𝗮𝗹 Latency, token limits, cost constraints. You’ll need caching, batching, fallbacks, and timeouts, not to mention cost monitoring for models that bill per token. Saying “Gen AI is just retrieval + prompt + API call” is like saying “Medicine is just diagnosis + treatment + prescription.” Sure, that’s the flow. But it misses all the nuance, risk, system thinking, and hard-earned experience required to build anything real If you're building Gen AI apps today, You’re not just coding; You're orchestrating chaos. Design for it. ♻️ Repost if this hit hard.

𝐌𝐨𝐬𝐭 𝐋𝐋𝐌 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐝𝐨𝐧’𝐭 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐰𝐞𝐚𝐤 𝐦𝐨𝐝𝐞𝐥𝐬 𝐨𝐫 𝐛𝐚𝐝 𝐩𝐫𝐨𝐦𝐩𝐭𝐬. 𝐓𝐡𝐞𝐲 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐛𝐫𝐨𝐤𝐞𝐧 𝐜𝐨𝐧𝐭𝐞𝐱𝐭. You can wire up tools, tune prompts, even use the best API but if your context engineering is sloppy, the system won’t scale or perform. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝟔 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐦𝐢𝐬𝐭𝐚𝐤𝐞𝐬 𝐭𝐡𝐚𝐭 𝐪𝐮𝐢𝐞𝐭𝐥𝐲 𝐛𝐫𝐞𝐚𝐤 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐋𝐋𝐌 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨 𝐢𝐧𝐬𝐭𝐞𝐚𝐝: 𝟏. 𝐀𝐝𝐝𝐢𝐧𝐠 𝐭𝐨𝐨 𝐦𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 More data ≠ better output. Overloading the context window makes the model noisy and distracted. Fix: Filter aggressively. Only load what’s relevant for the current step. 𝟐. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 Token limits are real. If you’re pushing raw text into prompts, you're wasting space. Fix: Add a compression layer. Summarize, distill, trim. Think like a systems engineer. 𝟑. 𝐌𝐢𝐱𝐢𝐧𝐠 𝐮𝐧𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 Blending topics, tools, or threads in one prompt dilutes accuracy. Fix: Scope context by task. Keep workflows logically isolated. 𝟒. 𝐍𝐨 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Outdated memory is worse than no memory. Most teams just log forever. Fix: Build a versioned context store. Use expiration and cleanup policies. 𝟓. 𝐓𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚𝐥𝐥 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐞𝐪𝐮𝐚𝐥𝐥𝐲 When everything is “important,” nothing is. Fix: Score and rank. Prioritize critical inputs. Downweight trivia. 𝟔. 𝐒𝐞𝐧𝐝𝐢𝐧𝐠 𝐫𝐚𝐰, 𝐮𝐧𝐟𝐢𝐥𝐭𝐞𝐫𝐞𝐝 𝐝𝐚𝐭𝐚 Dumping unstructured content straight into prompts kills clarity. Fix: Pre-process and route with intent. Structure before injection. 𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: Context isn’t just what you feed the model. It’s how you manage, shape, compress, and prioritize that information at every step. If you’re building agents, copilots, or decision engines, treat context like core architecture. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐢𝐭. 𝐃𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐝𝐮𝐦𝐩 𝐢𝐭. 𝐋𝐞𝐭 𝐦𝐞 𝐤𝐧𝐨𝐰 𝐢𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐚 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐭𝐨 𝐚𝐩𝐩𝐥𝐲 𝐭𝐡𝐢𝐬 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. #AgentBuildAI #Context #Prompting #GenAI

When debugging an LLM, treat it like a behavioral reliability problem, not just a software bug hunt. Hallucinations → probability-driven token completions without retrieval grounding. Contradictions → instability in output vectors under semantic or syntactic perturbations. Jailbreaks → prompt injection exploiting unprotected attention pathways. Approach: Run semantic diffing on paraphrased queries to detect drift. Stress-test with adversarial prompt sets and perturbation noise. Atomize outputs into factual units and verify against a trusted retrieval layer. If response consistency, adversarial resilience, and factual accuracy aren’t measurable, the model isn’t production-ready.

LLMs Are Failing Scientific Studies 31-50% of the Time, and NO prompt engineering wont help you 13M annotations across 37 real studies show LLMs produce wrong statistical conclusions in 1/3 to 1/2 of cases Even 70B parameter models fail 31% of the time - model scaling hits diminishing returns High annotation accuracy (93% F1) doesn't prevent downstream statistical failures (50% error rates) Researchers can manufacture any desired result by simply choosing different model/prompt combinations 100 human annotations outperform sophisticated LLM correction techniques for avoiding false discoveries The AI community has a reliability problem we're not discussing. Comprehensive testing of 18 LLMs across real research tasks reveals systematic unreliability for hypothesis testing. State-of-the-art models produce incorrect statistical conclusions 31% of the time. What doesn't work: Prompt engineering: <1% impact on reducing errors Model scaling beyond 70B: Plateaus with diminishing returns Using performance metrics as proxies: 93% accuracy still yields 50% statistical errors The manipulation risk: Basic model/prompt selection lets researchers manufacture false positives in 94% of null hypotheses and reverse effect directions in 68% of cases. The line between legitimate research and manipulation disappears. What works: Human annotations. Just 100 human labels outperform hybrid approaches for avoiding false discoveries. The bigger issue: 88% of reviewed studies recommend LLM usage while 43% provide no validation. We're automating workflows without understanding validity costs. This isn't anti-AI sentiment, it's calling for validation standards appropriate to the reliability we're actually getting, not what we hope for. Teams using LLMs: pre-register configurations, report all combinations tested, prioritize human validation, treat results as preliminary. The future of AI-assisted research needs honest assessment of limitations, not uncritical adoption. https://lnkd.in/dqmQGt5B #ArtificialIntelligence #MachineLearning #LLM #ResearchMethods #DataScience #ScientificResearch #ResearchIntegrity #DataValidation #StatisticalAnalysis #ComputationalSocialScience

LLMs Get Lost in Multi-turn Conversation The cat is out of the bag. Pay attention, devs. This is one of the most common issues when building with LLMs today. Glad there is now paper to share insights. Here are my notes: The paper investigates how LLMs perform in realistic, multi-turn conversational settings where user instructions are often underspecified and clarified over several turns. I keep telling devs to spend time preparing those initial instructions. Prompt engineering is important. The authors conduct large-scale simulations across 15 top LLMs (including GPT-4.1, Gemini 2.5 Pro, Claude 3.7 Sonnet, DeepSeek-R1, and others) over six generation tasks (code, math, SQL, API calls, data-to-text, and document summarization). Severe Performance Drop in Multi-Turn Settings All tested LLMs show significantly worse performance in multi-turn, underspecified conversations compared to single-turn, fully-specified instructions. The average performance drop is 39% across six tasks, even for SoTA models. For example, models with >90% accuracy in single-turn settings often drop to ~60% in multi-turn settings. Degradation Is Due to Unreliability, Not Just Aptitude The performance loss decomposes into a modest decrease in best-case capability (aptitude, -15%) and a dramatic increase in unreliability (+112%). In multi-turn settings, the gap between the best and worst response widens substantially, meaning LLMs become much less consistent and predictable. High-performing models in single-turn settings are just as unreliable as smaller models in multi-turn dialogues. Don't ignore testing and evaluating in multi-turn settings. Main reasons LLMs get "lost" - Make premature and often incorrect assumptions early in the conversation. - Attempt full solutions before having all necessary information, leading to “bloated” or off-target answers. - Over-rely on their previous (possibly incorrect) answers, compounding errors as the conversation progresses. - Produce overly verbose outputs, which can further muddle context and confuse subsequent turns. - Pay disproportionate attention to the first and last turns, neglecting information revealed in the middle turns (“loss-in-the-middle” effect). Practical Recommendations: - Users are better off consolidating all requirements into a single prompt rather than clarifying over multiple turns. - If a conversation goes off-track, starting a new session with a consolidated summary leads to better outcomes. - System builders and model developers are urged to prioritize reliability in multi-turn contexts, not just raw capability. This is especially true if you are building complex agentic systems where the impact of these issues is more prevalent. - LLMs are really weird. And all this weirdness is creeping up into the latest models too but it more subtle ways. Be careful out there, devs.

Microsoft & Salesforce tested 15 leading LLMs—including GPT-4, Claude, Gemini, and Llama. Multi-turn conversations lead to a catastrophic 39% performance drop. ————————— Imagine this: You carefully explain your problem to an AI assistant—step by step. It starts responding confidently. But then… it goes off track. You clarify again. It gets worse. Turns out, this isn't your fault. It’s a fundamental flaw in LLMs. Even the best models—those with near-perfect single-turn scores—fall apart in realistic, back-and-forth chats. They get lost. Literally. ————————— 🔍 Why Do LLMs Get Lost? The researchers simulated underspecified conversations (the way most humans naturally talk: a bit vague, gradually clarifying). Here’s what happened: 1. Premature Confidence LLMs jump to answers too early—before having enough information. 2. Snowballing Mistakes Once they make a wrong assumption, they cling to it—even as new info arrives. 3. Memory Collapse They forget the middle of the conversation, focusing only on the beginning and the end. 4. Bloating Responses Their answers get longer and longer… stuffing in irrelevant details that derail the chat. ————————— The Key Insight: It’s NOT a Problem of “IQ”—It’s a Problem of Stability Many think better models (like GPT-4 or Claude) won’t suffer from this. But the study found: Even the smartest models become unreliable in multi-turn settings. The issue isn’t just raw capability—it’s fragility. The main culprit? Unreliability skyrockets by 112% in multi-turn chats. This isn't about aptitude—it’s about consistency and resilience across turns. ————————— Can We Fix This? The study tested two solutions inspired by agent frameworks: Recap: At the end, summarize everything so far. Snowball: Re-state everything at each turn (like a growing checklist). Both helped—but didn’t fully solve the problem. ————————— Here’s the bottom line: We need LLMs that natively handle multi-turn, vague, evolving conversations. Not just LLMs that ace single-shot benchmarks. 🎯 Takeaway for Builders and Users: LLM Builders → Prioritize multi-turn reliability alongside performance. AI Engineers → Don’t blindly trust LLMs in multi-turn flows. Users → If your chat goes off-track? Start fresh. 🚀 Final Thought In the race to build super-intelligent LLMs, we might’ve overlooked something simpler—but just as crucial: Not getting lost in a simple conversation. That, my friends, might be the next frontier. ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Want to build Real-World AI agents? Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟰-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 TODAY! ➠ Build Real-World AI Agents for Healthcare, Finance, Aviation, Smart Cities ➠ Learn 4 Framework: LangGraph | PydanticAI | CrewAI | OpenAI Swarm ➠ Work with Text, Audio, Video and Tabular Data 👉𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟰𝟱% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁): https://lnkd.in/eGuWr4CH

Building LLM apps? Learn how to test them effectively and avoid common mistakes with this ultimate guide from LangChain! 🚀 This comprehensive document highlights: 1️⃣ Why testing matters: Tackling challenges like non-determinism, hallucinated outputs, and performance inconsistencies. 2️⃣ The three stages of the development cycle: 💥 Design: Incorporating self-corrective mechanisms for error handling (e.g., RAG systems and code generation). 💥Pre-Production: Building datasets, defining evaluation criteria, regression testing, and using advanced techniques like pairwise evaluation. 💥Post-Production: Monitoring performance, collecting feedback, and bootstrapping to improve future versions. 3️⃣ Self-corrective RAG applications: Using error handling flows to mitigate hallucinations and improve response relevance. 4️⃣ LLM-as-Judge: Automating evaluations while reducing human effort. 5️⃣ Real-time online evaluation: Ensuring your LLM stays robust in live environments. This guide offers actionable strategies for designing, testing, and monitoring your LLM applications efficiently. Check it out and level up your AI development process! 🔗📘 ------------ Add your thoughts in the comments below—I’d love to hear your perspective! Sarveshwaran Rajagopal #AI #LLM #LangChain #Testing #AIApplications