RAG Semantic In-Memory Cache

Caches LLM responses by query embedding similarity rather than exact string equality. If a new question is semantically identical (e.g. Cosine > 0.96), serves from cache in 2ms.

STAFF ENGINEER TAKEAWAY: Cuts LLM costs and response latency by up to 60% by serving semantically identical questions from cache.

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More Topics in 12. AI Engineering

📚RAG Pipeline (Retrieval-Augmented Generation)

Augments LLM prompt context by retrieving semantically relevant text chunks from private vector databases before generating answers.

🚪AI Gateway & Smart Fallback Proxy

A unified reverse proxy sitting between applications and LLM providers that manages semantic caching, cost rate-limiting, and automated failover.

🤖Agentic ReAct (Reasoning + Acting)

An autonomous agent paradigm that interleaves reasoning (Thought), tool execution (Action), and observation (Observation) to solve multi-step problems.

🔎Hybrid Search & Cross-Encoder Reranking

Combines sparse keyword search (BM25) with dense semantic vector search via Reciprocal Rank Fusion (RRF), followed by a Cohere cross-encoder reranker.