The LLM Landscape
Large Language Models have moved from research curiosity to production infrastructure in the span of two years. GPT-4, Claude, Gemini, and open-weight models like Llama give developers a menu of options that differ in cost, latency, context window, and capability. Choosing the right model is as important as any other architectural decision.
Prompt Engineering Fundamentals
A model is only as good as the instructions you give it. System prompts define the assistant's persona, constraints, and output format. Few-shot examples in the prompt can dramatically improve consistency. Chain-of-thought prompting — asking the model to reason step-by-step before answering — reduces hallucination on complex tasks.
Vector Databases and RAG
Retrieval-Augmented Generation solves the hallucination problem for domain-specific knowledge. You embed your documents into a vector space using an embedding model, store them in a vector DB like Pinecone or pgvector, and at query time retrieve the most semantically relevant chunks to stuff into the context window. The model answers based on real data rather than parametric memory.
Streaming and UX
Users expect chatbot responses to stream token-by-token, like watching someone type. Next.js supports streaming responses out of the box via the ReadableStream API and the Vercel AI SDK's useChat hook. Never block the UI waiting for a full response — stream it.
Conclusion
Building a production LLM application requires more than calling an API. Think carefully about prompt design, implement RAG for grounded answers, stream responses for good UX, and instrument everything with logging and evals to catch regressions as models update.