Context Engineering for LLMs: Build Reliable, Production-Ready RAG Systems

Author(s): Mahimai Raja J

Originally published on Towards AI.

Context Engineering for LLMs: Build Reliable, Production-Ready RAG Systems

Your Large Language Model (LLM) prototype felt like magic. But in production, the magic is gone. It hallucinates, gives vague answers, and costs are unpredictable. Sound familiar?

The article discusses the need for context engineering in the deployment of Large Language Models (LLMs), highlighting issues such as hallucinations and unpredictable costs that arise when these models transition from prototype to production. It emphasizes the importance of designing effective contexts that include user input, instructions, and formatted output to enhance the reliability and scalability of AI applications. Through various strategies like prompt engineering and context management techniques, the piece outlines how to successfully integrate LLMs into practical applications while maintaining performance and usability.

Read the full blog for free on Medium.

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Published via Towards AI