Retrieval-Augmented Context — Smart Knowledge-Based Context

I will build a retrieval-augmented context assembly system — retrieving the most relevant content from your knowledge base for each query, ranking retrieved chunks by relevance, reranking for diversity and coverage, and assembling the optimal context within your token budget. Context assembly for RAG is a more nuanced problem than retrieval — the top-k retrieved chunks by similarity often contain redundant content covering the same aspect from multiple sources; a context assembly strategy that combines relevance with diversity, fits the assembled context within the token budget, and places the most relevant content in attention-maximising positions outperforms naive top-k assembly.

The system covers semantic retrieval, chunk reranking for relevance and diversity, context budget-aware assembly, content placement optimisation within the window, and retrieval quality evaluation using RAGAS metrics.