Context Engineering for Agentic RAG
victorialslocum · x · 2026-07-10
The author points out that agentic RAG pipelines usually fail not because of poor prompts, but because of poor context, coining the term "context engineering" for this issue.
The post breaks down the five essential components of a production-ready agentic RAG system: query enhancement, retrieval, memory, tools, and agent orchestration. It emphasizes that chunking strategies, short/long-term memory design, and contextual handling of tool calls directly impact system performance.
The core takeaway is that many AI systems outperform others not because they use the largest models, but because they execute context engineering more effectively.
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