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From Markdown inputs to load-balanced LLM endpoints, this guide walks through key lessons learned while taking a financial AI prototype into production. Learn how structured prompts, JSON validation, exponential backoff, batching, observability tools, and smart frameworks like LiteLLM can transform your system into a scalable, resilient AI service.

Data pipeline Creating a pipeline that requires sending some events to be processed in the background keeping scalability in mind is one of the key requirements for building applications today. In order to process heavy/time consuming jobs in your current application it’s generally better to separate it from your core application, and one way to achieve is to use a pub/sub architecture. The idea here is as follows: Maintain a queue of messages A publisher pushes messages to the queue under a...