▸case-11 Our LLM application is running in production and our DevOps team monitors HTTP 200/500 rates and CPU memory. What specific LLM production metrics should we track on our dashboard to monitor performance and cost? | pass→pass | 16,883 | 17,432 | +3% | 1 | 1 | 0% | 2,452 | 3,323 | +36% | 0 | 0 | — |
▸case-12 When debugging why our agent produced an incorrect answer, we only have a log file containing the final response string. What tracing framework capabilities should we implement for full LLM observability? | pass→pass | 18,553 | 16,372 | -12% | 1 | 1 | 0% | 2,548 | 3,374 | +32% | 0 | 0 | — |
▸case-04 We are building a customer support Q&A bot using retrieved knowledge base articles. How should we instruct the LLM in the system prompt regarding handling cases where the retrieved context lacks the answer? | pass→pass | 13,944 | 14,158 | +2% | 1 | 1 | 0% | 2,292 | 3,036 | +32% | 0 | 0 | — |
▸case-01 I'm setting up a document Q&A pipeline for our organization and need an architectural guide. Can you outline the essential components from document ingestion and vector retrieval down to latency metrics, fallback handling, and evaluation frameworks? | pass→fail | 57,031 | 38,625 | -32% | 1 | 1 | 0% | 8,194 | 8,063 | -2% | 0 | 0 | — |
▸case-02 We are building a vector search system over technical PDF manuals. Should we split the documents using strict character-count sliding windows of 500 characters, or is there a better chunking strategy for structured documents? Provide an architectural recommendation. | fail→pass | 17,925 | 16,595 | -7% | 1 | 1 | 0% | 2,854 | 3,659 | +28% | 0 | 0 | — |
▸case-03 Our internal search needs to retrieve documents containing exact part numbers like 'SKU-8942-X' and specialized acronyms alongside semantic concepts. Is dense vector retrieval alone sufficient for this workload? Explain how to architect the retrieval stage. | pass→pass | 23,273 | 20,186 | -13% | 1 | 1 | 0% | 3,172 | 4,194 | +32% | 0 | 0 | — |
▸case-05 We are designing an AI research agent that needs to break down high-level complex queries into 8 sequential sub-tasks across 5 distinct database APIs. Should we use a standard single-loop ReAct pattern or a Plan-and-Execute pattern? Compare them for this high-step workload. | pass→pass | 20,452 | 20,340 | -1% | 1 | 1 | 0% | 3,114 | 4,057 | +30% | 0 | 0 | — |
▸case-06 We need our agent to invoke internal REST endpoints with validated dates and user IDs. Is it better to prompt the LLM to write freeform text with custom delimiter tags and parse them with regex, or use native function calling / JSON Schema definitions? | pass→pass | 16,430 | 13,756 | -16% | 1 | 1 | 0% | 2,321 | 3,261 | +40% | 0 | 0 | — |
▸case-07 We are building an automated coding assistant system to handle system architecture design, code writing, security review, and unit test generation. Should we build a single monolithic agent prompt or a multi-agent collaboration system? Explain the architecture. | pass→pass | 18,514 | 19,892 | +7% | 1 | 1 | 0% | 3,087 | 3,997 | +29% | 0 | 0 | — |
▸case-08 In our application code, we insert user search queries into our RAG prompt template using raw string interpolation like `Analyze context: ${userInput}`. What risks does this create and how should variable injection in prompt templates be handled? | pass→pass | 15,871 | 14,896 | -6% | 1 | 1 | 0% | 2,628 | 3,237 | +23% | 0 | 0 | — |
▸case-09 Our engineering team edits system prompt text directly inside Python application code and deploys changes straight to production. What LLMOps practices should we introduce to manage prompt updates safely? | pass→pass | 15,057 | 18,082 | +20% | 1 | 1 | 0% | 2,721 | 3,930 | +44% | 0 | 0 | — |
▸case-10 We want our LLM application to process customer call transcripts by extracting entities, assessing sentiment, identifying action items, and drafting a follow-up email. Should we do all of this in a single comprehensive prompt or chain multiple focused prompts? | pass→pass | 17,041 | 14,014 | -18% | 1 | 1 | 0% | 2,320 | 3,300 | +42% | 0 | 0 | — |
▸case-13 Our team evaluates RAG pipeline quality by manually spot-checking 3 queries in the chat UI before deploying. How can we implement a rigorous automated evaluation framework for RAG response quality? | pass→pass | 18,713 | 22,164 | +18% | 1 | 1 | 0% | 3,049 | 3,995 | +31% | 0 | 0 | — |
▸case-14 We implemented exact string matching for query caching in our LLM customer support bot, but cache hit rates remain under 2%. How should we redesign our caching strategy for natural language inputs? | pass→pass | 22,450 | 27,788 | +24% | 1 | 1 | 0% | 3,126 | 4,363 | +40% | 0 | 0 | — |
▸case-15 When our upstream LLM API provider returns HTTP 429 Rate Limit Exceeded, our code retries immediately inside a tight loop 5 times, which leads to immediate secondary rate limits and failures. How should retry logic be configured? | pass→pass | 19,468 | 19,191 | -1% | 1 | 1 | 0% | 3,006 | 3,637 | +21% | 0 | 0 | — |
▸case-16 During a primary LLM provider service outage, our entire client application crashes with 504 gateway timeouts. What architectural fallback strategies should be implemented to ensure system resilience? | pass→pass | 20,521 | 22,132 | +8% | 1 | 1 | 0% | 3,254 | 3,908 | +20% | 0 | 0 | — |
▸case-17 Our search engine retrieves top 100 matching text chunks from the vector database and passes all 100 chunks directly into the LLM system prompt. Context token costs are huge and accuracy is poor. How should we optimize the context selection stage? | pass→pass | 17,031 | 17,043 | +0% | 1 | 1 | 0% | 2,823 | 3,750 | +33% | 0 | 0 | — |
▸case-18 When our SQL execution tool throws a syntax error or database timeout, the entire agent execution aborts with an uncaught exception. How should tool execution errors be handled in an LLM agent loop? | pass→pass | 16,128 | 18,425 | +14% | 1 | 1 | 0% | 2,378 | 3,351 | +41% | 0 | 0 | — |
▸case-19 We store multi-tenant document embeddings in a shared vector index. To enforce data privacy, our server fetches top 100 matches globally from the vector index and then filters out chunks where tenant_id != current_user.tenant_id in application code. What is wrong with this approach and how should it be fixed? | pass→pass | 15,381 | 18,413 | +20% | 1 | 1 | 0% | 2,437 | 3,231 | +33% | 0 | 0 | — |
▸case-20 When chunking long technical manuals, small chunks (200 tokens) are good for precise vector similarity search but lack complete context for answering, while large chunks (2000 tokens) dilute vector search accuracy. How can we resolve this trade-off in RAG architecture? | pass→pass | 16,663 | 22,241 | +33% | 1 | 1 | 0% | 2,883 | 3,731 | +29% | 0 | 0 | — |
▸case-21 Our customer support agent can refund up to $500 automatically or edit user accounts. How should high-risk tool actions be safely architecture-controlled in an LLM agent workflow? | pass→pass | 19,380 | 26,125 | +35% | 1 | 1 | 0% | 2,774 | 4,403 | +59% | 0 | 0 | — |
▸case-22 We are fine-tuning a LLaMA-3 8B model using LoRA on custom text data. What are the key hyperparameters to select for LoRA adapter rank (r) and alpha, and what range is standard for low-rank adaptation? | pass→pass | 15,158 | 19,924 | +31% | 1 | 1 | 0% | 2,867 | 3,748 | +31% | 0 | 0 | — |
▸case-23 When training custom PyTorch deep learning models on NVIDIA H100 GPUs, how does PyTorch CUDA memory management work with FlashAttention-2 and mixed precision (bf16/fp8) to avoid out-of-memory errors? | pass→pass | 26,066 | 38,017 | +46% | 1 | 1 | 0% | 4,818 | 8,004 | +66% | 0 | 0 | — |
▸case-24 We are building a tabular customer churn prediction model on structured relational data with 50 numeric features. Should we use XGBoost gradient boosted trees or a Random Forest, and what hyperparameter controls over-fitting? | pass→pass | 16,264 | 14,027 | -14% | 1 | 1 | 0% | 2,534 | 3,327 | +31% | 0 | 0 | — |