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Towards Data Science0 Hot0 comments18 min read
Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer
Context engineering — the discipline of assembling everything that lands in an LLM's context window — is applied to a single-document RAG pipeline built from four typed bricks: document parsing, question parsing, retrieval, and generation. Each brick emits typed outputs (Pydantic models, DataFrames, audit dicts) that converge on one LLM call. The system prompt is fixed for caching, retrieved lines are filtered to a few thousand tokens, and doc-level metadata is serialized as compact JSON via a PromptContext aggregator. The term 'context engineering' was coined by Tobi Lütke and endorsed by Andrej Karpathy in June 2025. Practical implications include cheaper cached prompts, auditable per-call context reconstruction from persisted brick outputs, and a stable aggregator signature that can absorb corpus-context and conversation-context layers without breaking existing brick signatures.
Source: https://towardsdatascience.com/context-engineering-for-rag-the-four-typed-inputs-behind-every-rag-answer. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
