What is an LLM application?
An LLM application is a software product that uses a large language model (LLM) as a core component of its functionality not as a chatbot overlay, but as the intelligence engine that powers a specific workflow. Examples: a document Q&A system that answers questions from an enterprise knowledge base with cited sources (the LLM understands the question and generates an answer from retrieved documents); a contract analysis platform that extracts and compares clause terms across thousands of contracts (the LLM understands legal text and produces structured analysis); an AI writing assistant that generates on-brand sales emails from CRM context (the LLM generates personalised content matching company tone guidelines). LLM applications differ from chatbots in that they are task-specific, their outputs have defined structure and evaluation criteria, and they are integrated into business workflows rather than being standalone conversation interfaces.
What is the difference between LangChain and LlamaIndex?
LangChain and LlamaIndex are both LLM orchestration frameworks, but they have different design philosophies and strengths. LangChain is a general-purpose LLM application framework it provides abstractions for chains (sequences of LLM calls and other operations), agents (LLMs that decide which tools to call), memory (conversation history management), and tool integration. LangChain is the better choice for complex multi-step LLM workflows, agent-based systems, and applications requiring broad tool integration. LlamaIndex is specialised for data-intensive LLM applications specifically RAG systems. It excels at document ingestion, chunking strategies, index construction, query pipeline configuration, and RAG evaluation (RAGAS integration). LlamaIndex is the better choice when the primary use case is Q&A or analysis over a document corpus. ClickMasters uses LangChain for orchestration-heavy applications and LlamaIndex for RAG-heavy applications often combining both in the same system.
How do you evaluate LLM application quality?
LLM application evaluation uses automated and human evaluation methods. For RAG systems, RAGAS provides four automated metrics: Faithfulness (does the answer contain only information from the retrieved context no hallucinations?), Context Relevance (does the retrieved context actually contain information relevant to the question?), Answer Relevance (does the answer actually address the question asked?), and Context Recall (did the retrieval find all the relevant context?). For generation quality, DeepEval provides pytest-style unit tests for LLM outputs assert that a response contains specific information, does not contain specific words, is within a character length range, or matches a semantic pattern. LangSmith captures production traces real user queries and LLM responses can be reviewed, annotated, and used to build an evaluation dataset from production traffic. ClickMasters implements RAGAS or DeepEval evaluation as standard on all RAG and generation applications providing a quantitative quality baseline and a regression detection mechanism for future model or prompt changes.
How do you handle LLM latency in production?
LLM latency has two components: time-to-first-token (TTFT how long before the user sees any response) and generation speed (tokens per second how fast the full response appears). For user-facing features, streaming is essential: the backend starts forwarding tokens to the frontend as soon as the LLM begins generating the user sees the response start within 1-2 seconds even if the complete response takes 10-20 seconds. Without streaming, users see a blank screen until the full response is ready. At the architecture level, latency is managed with: model selection (GPT-4o mini has 3-5x lower TTFT than GPT-4o use the smaller model when the task does not require full capability), response caching (identical or semantically similar queries are served from cache zero LLM latency), prompt length optimisation (shorter prompts = faster responses reduce few-shot examples to the minimum required), and parallel retrieval (in RAG systems, retrieve from the vector database and any other data sources in parallel not sequentially).
