Content:

Large Language Models (LLMs) like GPT-4 are powerful, but integrating them into production is far from plug-and-play. From unpredictable responses to latency issues, real-world deployment comes with challenges.

Teams need to design with uncertainty in mind. LLMs don’t always return the same output, which means deterministic experiences (like form validation or rule-based flows) need guardrails or backup logic. Prompt engineering is an iterative process - a small tweak can drastically alter results.

Another key challenge: context length. When building chatbots or agents, you must manage the information passed into the prompt efficiently. Otherwise, you risk response drift or cutoff.

Security and compliance also matter. LLMs can leak sensitive data if prompts aren’t sanitized, and usage needs to be monitored for bias or incorrect answers - especially in regulated industries.