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Integrating AI into Production Workflows
Moving past the proof-of-concept phase. How to handle rate limits, hallucinations, and context chunking when putting LLMs in front of real users.
Samrat Sigdel··12 min read
Beyond the Wrapper\n\nIt's easy to build a wrapper around the OpenAI API. It's incredibly difficult to build a reliable AI feature that handles real-world edge cases gracefully.\n\n### Handling Rate Limits and Retries\n\nYou need a robust queuing and retry mechanism. When an LLM API goes down or rate-limits you, your application state shouldn't corrupt. Idempotency is crucial here.\n\n### Context Management\n\nRAG (Retrieval-Augmented Generation) is the standard approach, but naive vector search often fails. You need hybrid search (keyword + semantic) and intelligent context window management.
Topics:
LLMs
Production
Next.js
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