🤖 Modern LLM applications aren’t just a prompt — they’re complex systems
Shifting from “prompt thinking” to “systems thinking” is the biggest leap in LLM engineering.
🔑 The 10 key concepts:
- Context Engineering — what information to show the model, when, and in what order
- Tool Calling — the model doesn’t just talk: it acts (searches, runs code, calls APIs)
- MCP (Model Context Protocol) — standard for sharing tools across AI systems
- A2A Communication — how multiple agents coordinate with each other
- Semantic Caching — reuse responses for similar questions → lower cost and latency
- Contextual Compression — extract only relevant fragments from the context
- Reranking — reorder search results to promote the most relevant ones
- Hybrid Retrieval — combine semantic search + keyword (BM25) for better results
- Agent Memory Architecture — separate short-term (current task) and long-term (knowledge base) memory
- Inference Routing — route each query to the most appropriate model based on complexity and cost
📌 The key insight: 80% of production failures come not from the model, but from the system around it.
💡 Explanation in a nutshell#
A standalone LLM is like an expert locked in a room. LLM engineering is the system that lets it search for information, use tools, collaborate with other agents, and remember past conversations. Learning these 10 concepts means learning to build that system professionally.
More information at the link 👇
Also published on LinkedIn.
