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An LLM application combines a language model with product logic, data, tools, prompts, and user experience to solve a specific workflow. Rather than treating the model as a standal...
At the heart of any RAG system is the retrieval pipeline, and chunking is its starting point. Chunking splits documents into smaller passages that can be indexed and retrieved effe...
Structured output asks the model to return data in a predictable format such as JSON, making it easier to validate and consume by downstream software. To make this reliable in prac...
LLM applications often span many turns or sessions, which makes conversation memory important. Conversation memory stores relevant user or task context across turns or sessions, en...
Tool calling introduces real side effects, so tools should have narrow permissions, exposing only the actions and data needed for the task. This limits damage from mistakes or prom...
Once an LLM application is in production, observability becomes essential. Observability tracks requests, model choices, tools, latency, cost, and failures, making production LLM b...